Jeremy L. Warner
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View article: Trends in the design of FDA registrational randomised clinical trials in haematology and oncology: a 20-year cohort study
Trends in the design of FDA registrational randomised clinical trials in haematology and oncology: a 20-year cohort study Open
Introduction Registrational trials are clinical trials which are used to justify regulatory approval of a new medication by the FDA. However, clinical trials may not be designed around patient-centred outcomes, like overall survival (OS) o…
View article: A Field Guide to Deploying AI Agents in Clinical Practice
A Field Guide to Deploying AI Agents in Clinical Practice Open
Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implementation within clinical settings. To address this, we presen…
View article: Risk Factors for COVID-19–Related Hospitalization and Death in Patients With Cancer
Risk Factors for COVID-19–Related Hospitalization and Death in Patients With Cancer Open
Importance Retrospective case series have identified having cancer and receiving treatment for cancer as risk factors for inferior COVID-19 outcomes. Objective To determine risk factors for hospitalization and death in patients with cancer…
View article: Systemic Anticancer Therapy Timelines Extraction From Electronic Medical Records Text: Algorithm Development and Validation
Systemic Anticancer Therapy Timelines Extraction From Electronic Medical Records Text: Algorithm Development and Validation Open
Background The systemic treatment of cancer typically requires the use of multiple anticancer agents in combination or sequentially. Clinical narrative texts often contain extensive descriptions of the temporal sequencing of systemic antic…
View article: Reliability of Large Language Model Knowledge Across Brand and Generic Cancer Drug Names
Reliability of Large Language Model Knowledge Across Brand and Generic Cancer Drug Names Open
PURPOSE To evaluate the performance and consistency of large language models (LLMs) across brand and generic oncology drug names in various clinical tasks, addressing concerns about potential fluctuations in LLM performance because of subt…
View article: Obesity’s Disparate Impact on COVID-19 Outcomes in Asian American Patients with Cancer
Obesity’s Disparate Impact on COVID-19 Outcomes in Asian American Patients with Cancer Open
Data on COVID-19 outcomes in Asian Americans and Pacific Islanders (AAPI) are lacking. We analyzed data from 6,244 patients from the COVID-19 and Cancer Consortium, including 6.0% AAPI patients, to examine disparities in outcomes following…
View article: COVID-19 and Cancer
COVID-19 and Cancer Open
Importance: The COVID-19 pandemic has had consequences for patients with cancer worldwide and has been associated with delays in diagnosis, interruption of treatment and follow-up care, and increases in overall infection rates and prematur…
View article: Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis
Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis Open
Background Concise synopses of clinical evidence support treatment decision-making but are time-consuming to curate. Large language models (LLMs) offer potential but they may provide inaccurate information. We objectively assessed the abil…
View article: Racial Disparities in COVID-19 Outcomes Among Black and White Patients With Cancer
Racial Disparities in COVID-19 Outcomes Among Black and White Patients With Cancer Open
Importance: Non-Hispanic Black individuals experience a higher burden of COVID-19 than the general population; hence, there is an urgent need to characterize the unique clinical course and outcomes of COVID-19 in Black patients with cancer…
View article: Patients recently treated for B-lymphoid malignancies show increased risk of severe COVID-19: a CCC19 registry analysisImpact of B-cell malignancy therapy on COVID-19 outcomes
Patients recently treated for B-lymphoid malignancies show increased risk of severe COVID-19: a CCC19 registry analysisImpact of B-cell malignancy therapy on COVID-19 outcomes Open
Patients with B-lymphoid malignancies have been consistently identified as a population at high risk of severe COVID-19. Whether this is exclusively due to cancer-related deficits in humoral and cellular immunity, or whether risk of severe…
View article: 388 Subtyping social determinants of health in cancer: Implications for health equity policies
388 Subtyping social determinants of health in cancer: Implications for health equity policies Open
Objectives/Goals: Although several studies have identified significant associations between specific social determinants of health (SDoH) and adverse outcomes, little is known about how SDoH co-occur to form subtypes and their outcome-base…
View article: Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients
Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients Open
The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment…
View article: Coronavirus Disease 2019 (COVID-19) Real World Data Infrastructure: A Big-Data Resource for Study of the Impact of COVID-19 in Patient Populations With Immunocompromising Conditions
Coronavirus Disease 2019 (COVID-19) Real World Data Infrastructure: A Big-Data Resource for Study of the Impact of COVID-19 in Patient Populations With Immunocompromising Conditions Open
Background We developed a United States–based real-world data resource to better understand the continued impact of the coronavirus disease 2019 (COVID-19) pandemic on immunocompromised patients, who are typically underrepresented in prosp…
View article: Reliability of large language model knowledge across brand and generic cancer drug names
Reliability of large language model knowledge across brand and generic cancer drug names Open
Purpose To evaluate the performance and consistency of large language models (LLMs) across brand and generic oncology drug names in various clinical tasks, addressing concerns about potential fluctuations in LLM performance due to subtle p…
View article: Inferring gender from first names: Comparing the accuracy of Genderize, Gender API, and the gender R package on authors of diverse nationality
Inferring gender from first names: Comparing the accuracy of Genderize, Gender API, and the gender R package on authors of diverse nationality Open
Meta-researchers commonly leverage tools that infer gender from first names, especially when studying gender disparities. However, tools vary in their accuracy, ease of use, and cost. The objective of this study was to compare the accuracy…
View article: LUNAR: A Deep Learning Model to Predict Glioma Recurrence Using Integrated Genomic and Clinical Data
LUNAR: A Deep Learning Model to Predict Glioma Recurrence Using Integrated Genomic and Clinical Data Open
Gliomas account for approximately 25.5% of all primary brain and central nervous system tumors, with a striking 80.8% of these being malignant. The prognosis varies significantly; low-grade gliomas (LGGs) can exhibit 5-year survival rates …
View article: A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges
A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges Open
Summary: People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. …
View article: Collaborative Large Language Models for Automated Data Extraction in Living Systematic Reviews
Collaborative Large Language Models for Automated Data Extraction in Living Systematic Reviews Open
Objective Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) …
View article: Supplemental Fig. 1 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Fig. 1 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Figure 1: OS and PFS for KRAS-mutated NSCLC treated with first-line platinum-based chemotherapy.
View article: Supplemental Table 4 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Table 4 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Table 4
View article: Supplemental Table 3 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Table 3 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Table 3
View article: Supplemental Fig. 2 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Fig. 2 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Figure 2: OS and PFS for patients with NSCLC treated with first-line platinum-based chemotherapy.
View article: Supplemental Fig. 3 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Fig. 3 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Figure 3: PFS estimates for patients with NSCLC treated with standard first- and second-line therapies.
View article: Supplemental Table 4 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Table 4 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Table 4
View article: Supplemental Table 3 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Table 3 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Table 3
View article: Supplemental Table 2 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer
Supplemental Table 2 from The GENIE BPC NSCLC cohort: a real-world repository integrating standardized clinical and genomic data for 1,846 patients with non-small cell lung cancer Open
Supplemental Table 2