Machine learning prediction of anastomotic leak after low anterior resection: Nationwide database analysis Article Swipe
Takashi Sakamoto
,
Hideki Endo
,
Hiroyuki Yamamoto
,
Takashi Akiyoshi
,
Ken Shirabe
,
Hideki Ueno
,
Hiroshi Hasegawa
,
Takeshi Naitoh
,
Yosuke Fukunaga
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1097/md.0000000000043977
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1097/md.0000000000043977
Predicting anastomotic leak preoperatively in patients undergoing low anterior resection for rectal cancer remains a significant challenge. This study aims to develop and validate a predictive model for anastomotic leaks using a machine learning algorithm. Data were collected on patients who underwent low anterior resection from 2015 to 2021 from the National Clinical Database in Japan. The patients were divided into 2 cohorts: a derivation cohort and a validation cohort. The derivation cohort included patients who underwent surgery between January 2015 and December 2019, while the validation cohort included those from January 2020 to December 2021. Three models were developed: logistic regression, logistic regression with least absolute shrinkage and selection operator (Lasso regression), and eXtreme gradient boosting model. We calculated the area under the receiver operating characteristic curve (AUROC) and compared it with the logistic regression model using the DeLong test. A total of 119,818 eligible patients were identified. The incidence of anastomotic leaks was 9.6% in the derivation cohort and 8.4% in the validation cohort, respectively. The predictive ability for the validation cohort using logistic regression (AUROC 0.6324, 95% confidence interval [CI] 0.6220–0.6427, reference) was similar to that of Lasso regression (AUROC 0.6333, 95% CI 0.6229–0.6436, P = .13) and eXtreme gradient boosting (AUROC 0.6333, 95% CI 0.6230–0.6437, P = .41). Machine learning prediction model for anastomotic leak using preoperative information routinely inputted in the National Clinical Database, showed suboptimal prediction ability. It wound be possible to share the fact with patients that preoperative prediction of anastomotic leak is difficult.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/md.0000000000043977
- OA Status
- gold
- Cited By
- 1
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413921721
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413921721Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1097/md.0000000000043977Digital Object Identifier
- Title
-
Machine learning prediction of anastomotic leak after low anterior resection: Nationwide database analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-22Full publication date if available
- Authors
-
Takashi Sakamoto, Hideki Endo, Hiroyuki Yamamoto, Takashi Akiyoshi, Ken Shirabe, Hideki Ueno, Hiroshi Hasegawa, Takeshi Naitoh, Yosuke FukunagaList of authors in order
- Landing page
-
https://doi.org/10.1097/md.0000000000043977Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1097/md.0000000000043977Direct OA link when available
- Concepts
-
Medicine, Logistic regression, Cohort, Lasso (programming language), Receiver operating characteristic, Confidence interval, Anastomosis, Cohort study, Surgery, Internal medicine, Computer science, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4413921721 |
|---|---|
| doi | https://doi.org/10.1097/md.0000000000043977 |
| ids.doi | https://doi.org/10.1097/md.0000000000043977 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40859558 |
| ids.openalex | https://openalex.org/W4413921721 |
| fwci | 3.27053909 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | Q000453 |
| mesh[1].descriptor_ui | D057868 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | epidemiology |
| mesh[1].descriptor_name | Anastomotic Leak |
| mesh[2].qualifier_ui | Q000175 |
| mesh[2].descriptor_ui | D057868 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | diagnosis |
| mesh[2].descriptor_name | Anastomotic Leak |
| mesh[3].qualifier_ui | Q000209 |
| mesh[3].descriptor_ui | D057868 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | etiology |
| mesh[3].descriptor_name | Anastomotic Leak |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000069550 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Machine Learning |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D008297 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Male |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D005260 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Female |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008875 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Middle Aged |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D000368 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Aged |
| mesh[9].qualifier_ui | Q000601 |
| mesh[9].descriptor_ui | D012004 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | surgery |
| mesh[9].descriptor_name | Rectal Neoplasms |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D016208 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Databases, Factual |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D016015 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Logistic Models |
| mesh[12].qualifier_ui | Q000453 |
| mesh[12].descriptor_ui | D007564 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | epidemiology |
| mesh[12].descriptor_name | Japan |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D012372 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | ROC Curve |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D012189 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Retrospective Studies |
| type | article |
| title | Machine learning prediction of anastomotic leak after low anterior resection: Nationwide database analysis |
| biblio.issue | 34 |
| biblio.volume | 104 |
| biblio.last_page | e43977 |
| biblio.first_page | e43977 |
| topics[0].id | https://openalex.org/T10335 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2730 |
| topics[0].subfield.display_name | Oncology |
| topics[0].display_name | Colorectal Cancer Surgical Treatments |
| topics[1].id | https://openalex.org/T10552 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.995199978351593 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2730 |
| topics[1].subfield.display_name | Oncology |
| topics[1].display_name | Colorectal Cancer Screening and Detection |
| topics[2].id | https://openalex.org/T11930 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9848999977111816 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2705 |
| topics[2].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[2].display_name | Cardiac, Anesthesia and Surgical Outcomes |
| is_xpac | False |
| apc_list.value | 1950 |
| apc_list.currency | USD |
| apc_list.value_usd | 1950 |
| apc_paid.value | 1950 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1950 |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8805317878723145 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C151956035 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7592971920967102 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1132755 |
| concepts[1].display_name | Logistic regression |
| concepts[2].id | https://openalex.org/C72563966 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6881199479103088 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1303415 |
| concepts[2].display_name | Cohort |
| concepts[3].id | https://openalex.org/C37616216 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6641911268234253 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3218363 |
| concepts[3].display_name | Lasso (programming language) |
| concepts[4].id | https://openalex.org/C58471807 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6458496451377869 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q327120 |
| concepts[4].display_name | Receiver operating characteristic |
| concepts[5].id | https://openalex.org/C44249647 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5620964765548706 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q208498 |
| concepts[5].display_name | Confidence interval |
| concepts[6].id | https://openalex.org/C8443397 |
| concepts[6].level | 2 |
| concepts[6].score | 0.48950231075286865 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q15177 |
| concepts[6].display_name | Anastomosis |
| concepts[7].id | https://openalex.org/C201903717 |
| concepts[7].level | 2 |
| concepts[7].score | 0.43408408761024475 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1778788 |
| concepts[7].display_name | Cohort study |
| concepts[8].id | https://openalex.org/C141071460 |
| concepts[8].level | 1 |
| concepts[8].score | 0.396428644657135 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q40821 |
| concepts[8].display_name | Surgery |
| concepts[9].id | https://openalex.org/C126322002 |
| concepts[9].level | 1 |
| concepts[9].score | 0.30247217416763306 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[9].display_name | Internal medicine |
| concepts[10].id | https://openalex.org/C41008148 |
| concepts[10].level | 0 |
| concepts[10].score | 0.07678249478340149 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[10].display_name | Computer science |
| concepts[11].id | https://openalex.org/C136764020 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[11].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.8805317878723145 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/logistic-regression |
| keywords[1].score | 0.7592971920967102 |
| keywords[1].display_name | Logistic regression |
| keywords[2].id | https://openalex.org/keywords/cohort |
| keywords[2].score | 0.6881199479103088 |
| keywords[2].display_name | Cohort |
| keywords[3].id | https://openalex.org/keywords/lasso |
| keywords[3].score | 0.6641911268234253 |
| keywords[3].display_name | Lasso (programming language) |
| keywords[4].id | https://openalex.org/keywords/receiver-operating-characteristic |
| keywords[4].score | 0.6458496451377869 |
| keywords[4].display_name | Receiver operating characteristic |
| keywords[5].id | https://openalex.org/keywords/confidence-interval |
| keywords[5].score | 0.5620964765548706 |
| keywords[5].display_name | Confidence interval |
| keywords[6].id | https://openalex.org/keywords/anastomosis |
| keywords[6].score | 0.48950231075286865 |
| keywords[6].display_name | Anastomosis |
| keywords[7].id | https://openalex.org/keywords/cohort-study |
| keywords[7].score | 0.43408408761024475 |
| keywords[7].display_name | Cohort study |
| keywords[8].id | https://openalex.org/keywords/surgery |
| keywords[8].score | 0.396428644657135 |
| keywords[8].display_name | Surgery |
| keywords[9].id | https://openalex.org/keywords/internal-medicine |
| keywords[9].score | 0.30247217416763306 |
| keywords[9].display_name | Internal medicine |
| keywords[10].id | https://openalex.org/keywords/computer-science |
| keywords[10].score | 0.07678249478340149 |
| keywords[10].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.1097/md.0000000000043977 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S28966261 |
| locations[0].source.issn | 0025-7974, 1536-5964 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0025-7974 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Medicine |
| locations[0].source.host_organization | https://openalex.org/P4310318547 |
| locations[0].source.host_organization_name | Wolters Kluwer |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310318547 |
| locations[0].source.host_organization_lineage_names | Wolters Kluwer |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Medicine |
| locations[0].landing_page_url | https://doi.org/10.1097/md.0000000000043977 |
| locations[1].id | pmid:40859558 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Medicine |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40859558 |
| locations[2].id | pmh:oai:europepmc.org:11192513 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400806 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Europe PMC (PubMed Central) |
| locations[2].source.host_organization | https://openalex.org/I1303153112 |
| locations[2].source.host_organization_name | European Bioinformatics Institute |
| locations[2].source.host_organization_lineage | https://openalex.org/I1303153112 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12384797 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5028483150 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7483-9704 |
| authorships[0].author.display_name | Takashi Sakamoto |
| authorships[0].countries | JP |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I42570316 |
| authorships[0].affiliations[0].raw_affiliation_string | Takashi Sakamoto, Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo 1350081, Japan (e-mail |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I79072001 |
| authorships[0].affiliations[1].raw_affiliation_string | Department of Public Health, Graduate School of Medicine, International University of Health and Welfare, Tokyo, Japan |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I74801974 |
| authorships[0].affiliations[2].raw_affiliation_string | Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
| authorships[0].institutions[0].id | https://openalex.org/I79072001 |
| authorships[0].institutions[0].ror | https://ror.org/053d3tv41 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I79072001 |
| authorships[0].institutions[0].country_code | JP |
| authorships[0].institutions[0].display_name | International University of Health and Welfare |
| authorships[0].institutions[1].id | https://openalex.org/I42570316 |
| authorships[0].institutions[1].ror | https://ror.org/00bv64a69 |
| authorships[0].institutions[1].type | nonprofit |
| authorships[0].institutions[1].lineage | https://openalex.org/I42570316 |
| authorships[0].institutions[1].country_code | JP |
| authorships[0].institutions[1].display_name | Japanese Foundation For Cancer Research |
| authorships[0].institutions[2].id | https://openalex.org/I74801974 |
| authorships[0].institutions[2].ror | https://ror.org/057zh3y96 |
| authorships[0].institutions[2].type | education |
| authorships[0].institutions[2].lineage | https://openalex.org/I74801974 |
| authorships[0].institutions[2].country_code | JP |
| authorships[0].institutions[2].display_name | The University of Tokyo |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Takashi Sakamoto |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, Department of Public Health, Graduate School of Medicine, International University of Health and Welfare, Tokyo, Japan, Takashi Sakamoto, Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo 1350081, Japan (e-mail |
| authorships[1].author.id | https://openalex.org/A5086072560 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0052-0332 |
| authorships[1].author.display_name | Hideki Endo |
| authorships[1].countries | JP |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I74801974 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
| authorships[1].institutions[0].id | https://openalex.org/I74801974 |
| authorships[1].institutions[0].ror | https://ror.org/057zh3y96 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I74801974 |
| authorships[1].institutions[0].country_code | JP |
| authorships[1].institutions[0].display_name | The University of Tokyo |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hideki Endo |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
| authorships[2].author.id | https://openalex.org/A5101428124 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2447-7770 |
| authorships[2].author.display_name | Hiroyuki Yamamoto |
| authorships[2].countries | JP |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I74801974 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
| authorships[2].institutions[0].id | https://openalex.org/I74801974 |
| authorships[2].institutions[0].ror | https://ror.org/057zh3y96 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I74801974 |
| authorships[2].institutions[0].country_code | JP |
| authorships[2].institutions[0].display_name | The University of Tokyo |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hiroyuki Yamamoto |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
| authorships[3].author.id | https://openalex.org/A5012769131 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-2321-1412 |
| authorships[3].author.display_name | Takashi Akiyoshi |
| authorships[3].countries | JP |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I42570316 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan |
| authorships[3].institutions[0].id | https://openalex.org/I42570316 |
| authorships[3].institutions[0].ror | https://ror.org/00bv64a69 |
| authorships[3].institutions[0].type | nonprofit |
| authorships[3].institutions[0].lineage | https://openalex.org/I42570316 |
| authorships[3].institutions[0].country_code | JP |
| authorships[3].institutions[0].display_name | Japanese Foundation For Cancer Research |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Takashi Akiyoshi |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan |
| authorships[4].author.id | https://openalex.org/A5097810207 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Ken Shirabe |
| authorships[4].countries | JP |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210109427 |
| authorships[4].affiliations[0].raw_affiliation_string | The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I165735259 |
| authorships[4].affiliations[1].raw_affiliation_string | Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgical Science, Graduate School of Medicine, Gunma University, Maebashi, Gunma, Japan |
| authorships[4].institutions[0].id | https://openalex.org/I165735259 |
| authorships[4].institutions[0].ror | https://ror.org/046fm7598 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I165735259 |
| authorships[4].institutions[0].country_code | JP |
| authorships[4].institutions[0].display_name | Gunma University |
| authorships[4].institutions[1].id | https://openalex.org/I4210109427 |
| authorships[4].institutions[1].ror | https://ror.org/021fq8s19 |
| authorships[4].institutions[1].type | other |
| authorships[4].institutions[1].lineage | https://openalex.org/I4210109427 |
| authorships[4].institutions[1].country_code | JP |
| authorships[4].institutions[1].display_name | The Japanese Society of Gastroenterological Surgery |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Ken Shirabe |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgical Science, Graduate School of Medicine, Gunma University, Maebashi, Gunma, Japan, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[5].author.id | https://openalex.org/A5054771200 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-8600-1199 |
| authorships[5].author.display_name | Hideki Ueno |
| authorships[5].countries | JP |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I53888640 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Surgery, National Defense Medical College, Tokorozawa, Saitama, Japan |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I4210109427 |
| authorships[5].affiliations[1].raw_affiliation_string | Database Committee, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[5].institutions[0].id | https://openalex.org/I53888640 |
| authorships[5].institutions[0].ror | https://ror.org/02e4qbj88 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I53888640 |
| authorships[5].institutions[0].country_code | JP |
| authorships[5].institutions[0].display_name | National Defense Medical College |
| authorships[5].institutions[1].id | https://openalex.org/I4210109427 |
| authorships[5].institutions[1].ror | https://ror.org/021fq8s19 |
| authorships[5].institutions[1].type | other |
| authorships[5].institutions[1].lineage | https://openalex.org/I4210109427 |
| authorships[5].institutions[1].country_code | JP |
| authorships[5].institutions[1].display_name | The Japanese Society of Gastroenterological Surgery |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Hideki Ueno |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Database Committee, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan, Department of Surgery, National Defense Medical College, Tokorozawa, Saitama, Japan |
| authorships[6].author.id | https://openalex.org/A5073121403 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-1545-0509 |
| authorships[6].author.display_name | Hiroshi Hasegawa |
| authorships[6].countries | JP |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210109427 |
| authorships[6].affiliations[0].raw_affiliation_string | Project Management Subcommittee, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I65837984 |
| authorships[6].affiliations[1].raw_affiliation_string | Division of Gastrointestinal Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan |
| authorships[6].institutions[0].id | https://openalex.org/I65837984 |
| authorships[6].institutions[0].ror | https://ror.org/03tgsfw79 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I65837984 |
| authorships[6].institutions[0].country_code | JP |
| authorships[6].institutions[0].display_name | Kobe University |
| authorships[6].institutions[1].id | https://openalex.org/I4210109427 |
| authorships[6].institutions[1].ror | https://ror.org/021fq8s19 |
| authorships[6].institutions[1].type | other |
| authorships[6].institutions[1].lineage | https://openalex.org/I4210109427 |
| authorships[6].institutions[1].country_code | JP |
| authorships[6].institutions[1].display_name | The Japanese Society of Gastroenterological Surgery |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Hiroshi Hasegawa |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Division of Gastrointestinal Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan, Project Management Subcommittee, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[7].author.id | https://openalex.org/A5062435576 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-4169-6334 |
| authorships[7].author.display_name | Takeshi Naitoh |
| authorships[7].countries | JP |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I64189623 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Lower Gastrointestinal Surgery, Kitasato University School of Medicine, Sagamihara, Kanagawa, Japan. |
| authorships[7].affiliations[1].institution_ids | https://openalex.org/I4210109427 |
| authorships[7].affiliations[1].raw_affiliation_string | Project Management Subcommittee, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[7].institutions[0].id | https://openalex.org/I64189623 |
| authorships[7].institutions[0].ror | https://ror.org/00f2txz25 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I64189623 |
| authorships[7].institutions[0].country_code | JP |
| authorships[7].institutions[0].display_name | Kitasato University |
| authorships[7].institutions[1].id | https://openalex.org/I4210109427 |
| authorships[7].institutions[1].ror | https://ror.org/021fq8s19 |
| authorships[7].institutions[1].type | other |
| authorships[7].institutions[1].lineage | https://openalex.org/I4210109427 |
| authorships[7].institutions[1].country_code | JP |
| authorships[7].institutions[1].display_name | The Japanese Society of Gastroenterological Surgery |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Takeshi Naitoh |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Lower Gastrointestinal Surgery, Kitasato University School of Medicine, Sagamihara, Kanagawa, Japan., Project Management Subcommittee, The Japanese Society of Gastroenterological Surgery, Tokyo, Japan |
| authorships[8].author.id | https://openalex.org/A5074804849 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-6397-0907 |
| authorships[8].author.display_name | Yosuke Fukunaga |
| authorships[8].countries | JP |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I42570316 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan |
| authorships[8].institutions[0].id | https://openalex.org/I42570316 |
| authorships[8].institutions[0].ror | https://ror.org/00bv64a69 |
| authorships[8].institutions[0].type | nonprofit |
| authorships[8].institutions[0].lineage | https://openalex.org/I42570316 |
| authorships[8].institutions[0].country_code | JP |
| authorships[8].institutions[0].display_name | Japanese Foundation For Cancer Research |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Yosuke Fukunaga |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1097/md.0000000000043977 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine learning prediction of anastomotic leak after low anterior resection: Nationwide database analysis |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10335 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2730 |
| primary_topic.subfield.display_name | Oncology |
| primary_topic.display_name | Colorectal Cancer Surgical Treatments |
| related_works | https://openalex.org/W3030740161, https://openalex.org/W4408511902, https://openalex.org/W2153618163, https://openalex.org/W4385649027, https://openalex.org/W2418199038, https://openalex.org/W2091850843, https://openalex.org/W4367060559, https://openalex.org/W4388886454, https://openalex.org/W2386767720, https://openalex.org/W2066363065 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1097/md.0000000000043977 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S28966261 |
| best_oa_location.source.issn | 0025-7974, 1536-5964 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0025-7974 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Medicine |
| best_oa_location.source.host_organization | https://openalex.org/P4310318547 |
| best_oa_location.source.host_organization_name | Wolters Kluwer |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310318547 |
| best_oa_location.source.host_organization_lineage_names | Wolters Kluwer |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Medicine |
| best_oa_location.landing_page_url | https://doi.org/10.1097/md.0000000000043977 |
| primary_location.id | doi:10.1097/md.0000000000043977 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S28966261 |
| primary_location.source.issn | 0025-7974, 1536-5964 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0025-7974 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Medicine |
| primary_location.source.host_organization | https://openalex.org/P4310318547 |
| primary_location.source.host_organization_name | Wolters Kluwer |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310318547 |
| primary_location.source.host_organization_lineage_names | Wolters Kluwer |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Medicine |
| primary_location.landing_page_url | https://doi.org/10.1097/md.0000000000043977 |
| publication_date | 2025-08-22 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3214906937, https://openalex.org/W3006703643, https://openalex.org/W4382517185, https://openalex.org/W4239562689, https://openalex.org/W2793884781, https://openalex.org/W2581622720, https://openalex.org/W4306855755, https://openalex.org/W3199397394, https://openalex.org/W3200711733, https://openalex.org/W2740795020, https://openalex.org/W4390674087, https://openalex.org/W4390876555, https://openalex.org/W2328176404, https://openalex.org/W2126049444, https://openalex.org/W4210648125, https://openalex.org/W4318927621, https://openalex.org/W2042072098, https://openalex.org/W2074979724, https://openalex.org/W3020602981, https://openalex.org/W4302013406, https://openalex.org/W4410952656, https://openalex.org/W2766869780, https://openalex.org/W4313454827, https://openalex.org/W4404611607, https://openalex.org/W4377565314, https://openalex.org/W3119845049, https://openalex.org/W4295442671 |
| referenced_works_count | 27 |
| abstract_inverted_index.2 | 61 |
| abstract_inverted_index.= | 198, 210 |
| abstract_inverted_index.A | 141 |
| abstract_inverted_index.P | 197, 209 |
| abstract_inverted_index.a | 14, 24, 31, 63, 67 |
| abstract_inverted_index.CI | 195, 207 |
| abstract_inverted_index.It | 233 |
| abstract_inverted_index.We | 118 |
| abstract_inverted_index.be | 235 |
| abstract_inverted_index.in | 4, 54, 156, 162, 224 |
| abstract_inverted_index.is | 249 |
| abstract_inverted_index.it | 131 |
| abstract_inverted_index.of | 143, 151, 189, 246 |
| abstract_inverted_index.on | 38 |
| abstract_inverted_index.to | 20, 47, 93, 187, 237 |
| abstract_inverted_index.95% | 179, 194, 206 |
| abstract_inverted_index.The | 56, 70, 149, 167 |
| abstract_inverted_index.and | 22, 66, 81, 108, 113, 129, 160, 200 |
| abstract_inverted_index.for | 10, 27, 170, 216 |
| abstract_inverted_index.low | 7, 42 |
| abstract_inverted_index.the | 50, 85, 120, 123, 133, 138, 157, 163, 171, 225, 239 |
| abstract_inverted_index.was | 154, 185 |
| abstract_inverted_index.who | 40, 75 |
| abstract_inverted_index..13) | 199 |
| abstract_inverted_index.2015 | 46, 80 |
| abstract_inverted_index.2020 | 92 |
| abstract_inverted_index.2021 | 48 |
| abstract_inverted_index.8.4% | 161 |
| abstract_inverted_index.9.6% | 155 |
| abstract_inverted_index.Data | 35 |
| abstract_inverted_index.This | 17 |
| abstract_inverted_index.[CI] | 182 |
| abstract_inverted_index.aims | 19 |
| abstract_inverted_index.area | 121 |
| abstract_inverted_index.fact | 240 |
| abstract_inverted_index.from | 45, 49, 90 |
| abstract_inverted_index.into | 60 |
| abstract_inverted_index.leak | 2, 218, 248 |
| abstract_inverted_index.that | 188, 243 |
| abstract_inverted_index.were | 36, 58, 98, 147 |
| abstract_inverted_index.with | 104, 132, 241 |
| abstract_inverted_index..41). | 211 |
| abstract_inverted_index.2019, | 83 |
| abstract_inverted_index.2021. | 95 |
| abstract_inverted_index.Lasso | 190 |
| abstract_inverted_index.Three | 96 |
| abstract_inverted_index.curve | 127 |
| abstract_inverted_index.leaks | 29, 153 |
| abstract_inverted_index.least | 105 |
| abstract_inverted_index.model | 26, 136, 215 |
| abstract_inverted_index.share | 238 |
| abstract_inverted_index.study | 18 |
| abstract_inverted_index.test. | 140 |
| abstract_inverted_index.those | 89 |
| abstract_inverted_index.total | 142 |
| abstract_inverted_index.under | 122 |
| abstract_inverted_index.using | 30, 137, 174, 219 |
| abstract_inverted_index.while | 84 |
| abstract_inverted_index.wound | 234 |
| abstract_inverted_index.(AUROC | 177, 192, 204 |
| abstract_inverted_index.(Lasso | 111 |
| abstract_inverted_index.DeLong | 139 |
| abstract_inverted_index.Japan. | 55 |
| abstract_inverted_index.cancer | 12 |
| abstract_inverted_index.cohort | 65, 72, 87, 159, 173 |
| abstract_inverted_index.model. | 117 |
| abstract_inverted_index.models | 97 |
| abstract_inverted_index.rectal | 11 |
| abstract_inverted_index.showed | 229 |
| abstract_inverted_index.(AUROC) | 128 |
| abstract_inverted_index.0.6324, | 178 |
| abstract_inverted_index.0.6333, | 193, 205 |
| abstract_inverted_index.119,818 | 144 |
| abstract_inverted_index.January | 79, 91 |
| abstract_inverted_index.Machine | 212 |
| abstract_inverted_index.ability | 169 |
| abstract_inverted_index.between | 78 |
| abstract_inverted_index.cohort, | 165 |
| abstract_inverted_index.cohort. | 69 |
| abstract_inverted_index.develop | 21 |
| abstract_inverted_index.divided | 59 |
| abstract_inverted_index.eXtreme | 114, 201 |
| abstract_inverted_index.machine | 32 |
| abstract_inverted_index.remains | 13 |
| abstract_inverted_index.similar | 186 |
| abstract_inverted_index.surgery | 77 |
| abstract_inverted_index.Clinical | 52, 227 |
| abstract_inverted_index.Database | 53 |
| abstract_inverted_index.December | 82, 94 |
| abstract_inverted_index.National | 51, 226 |
| abstract_inverted_index.ability. | 232 |
| abstract_inverted_index.absolute | 106 |
| abstract_inverted_index.anterior | 8, 43 |
| abstract_inverted_index.boosting | 116, 203 |
| abstract_inverted_index.cohorts: | 62 |
| abstract_inverted_index.compared | 130 |
| abstract_inverted_index.eligible | 145 |
| abstract_inverted_index.gradient | 115, 202 |
| abstract_inverted_index.included | 73, 88 |
| abstract_inverted_index.inputted | 223 |
| abstract_inverted_index.interval | 181 |
| abstract_inverted_index.learning | 33, 213 |
| abstract_inverted_index.logistic | 100, 102, 134, 175 |
| abstract_inverted_index.operator | 110 |
| abstract_inverted_index.patients | 5, 39, 57, 74, 146, 242 |
| abstract_inverted_index.possible | 236 |
| abstract_inverted_index.receiver | 124 |
| abstract_inverted_index.validate | 23 |
| abstract_inverted_index.Database, | 228 |
| abstract_inverted_index.collected | 37 |
| abstract_inverted_index.incidence | 150 |
| abstract_inverted_index.operating | 125 |
| abstract_inverted_index.resection | 9, 44 |
| abstract_inverted_index.routinely | 222 |
| abstract_inverted_index.selection | 109 |
| abstract_inverted_index.shrinkage | 107 |
| abstract_inverted_index.underwent | 41, 76 |
| abstract_inverted_index.Predicting | 0 |
| abstract_inverted_index.algorithm. | 34 |
| abstract_inverted_index.calculated | 119 |
| abstract_inverted_index.challenge. | 16 |
| abstract_inverted_index.confidence | 180 |
| abstract_inverted_index.derivation | 64, 71, 158 |
| abstract_inverted_index.developed: | 99 |
| abstract_inverted_index.difficult. | 250 |
| abstract_inverted_index.prediction | 214, 231, 245 |
| abstract_inverted_index.predictive | 25, 168 |
| abstract_inverted_index.reference) | 184 |
| abstract_inverted_index.regression | 103, 135, 176, 191 |
| abstract_inverted_index.suboptimal | 230 |
| abstract_inverted_index.undergoing | 6 |
| abstract_inverted_index.validation | 68, 86, 164, 172 |
| abstract_inverted_index.anastomotic | 1, 28, 152, 217, 247 |
| abstract_inverted_index.identified. | 148 |
| abstract_inverted_index.information | 221 |
| abstract_inverted_index.regression, | 101 |
| abstract_inverted_index.significant | 15 |
| abstract_inverted_index.preoperative | 220, 244 |
| abstract_inverted_index.regression), | 112 |
| abstract_inverted_index.respectively. | 166 |
| abstract_inverted_index.characteristic | 126 |
| abstract_inverted_index.preoperatively | 3 |
| abstract_inverted_index.0.6220–0.6427, | 183 |
| abstract_inverted_index.0.6229–0.6436, | 196 |
| abstract_inverted_index.0.6230–0.6437, | 208 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5028483150 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I42570316, https://openalex.org/I74801974, https://openalex.org/I79072001 |
| citation_normalized_percentile.value | 0.88634029 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |