Platelet Count Estimation on Peripheral Smear Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.4103/jme.jme_36_20
Context: Automated cell counters have revolutionised laboratory medicine and are being used worldwide. They give accurate results, can run large number of samples and generate accurate results within a short span of time. Quality check procedures in this equipment ensure accuracy of results. However, for platelet counts, the pathologist is still dependent on confirming the results by manual methods, especially in situations of low platelet count and flagging by the electronic counter. Different laboratories use different methods for manual estimation of platelet counts. Aims: This study was done to compare the platelet counts assessed on peripheral smear examination by different multiplication factors, and to identify the multiplication factor which derives the most accurate platelet count. Settings and Design: This was an observational study. Methods: Peripheral smears prepared from 100 ethylenediaminetetraacetic acid-anticoagulated blood samples were manually examined under oil immersion field (OIF) for platelets. Platelet count was further calculated by multiplying the number of platelets per OIF using multiplication factors of 10,000, 15,000 and 20,000. This was compared with platelet (PLT)/red blood cell (RBC) method and automated platelet counts. Accuracy of various manual methods was evaluated by calculating the per cent difference of the methods with automated analyser counts. Statistical Analysis Used: Descriptive statistics was used for statistical analysis. Results: Manual platelet count estimation on peripheral smear employing multiplication factor of 15,000 was found to have comparable accuracy to analyser counts, followed by the PLT/RBC ratio method. Conclusions: This study confirms that for manual estimation of platelet counts on peripheral smear, a standard multiplication factor of 15,000 on OIF gives the most accurate result, which is closest to counts obtained by automated analysers.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4103/jme.jme_36_20
- OA Status
- diamond
- References
- 15
- Related Works
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- OpenAlex ID
- https://openalex.org/W4285414287
Raw OpenAlex JSON
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https://openalex.org/W4285414287Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4103/jme.jme_36_20Digital Object Identifier
- Title
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Platelet Count Estimation on Peripheral SmearWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
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2022-05-01Full publication date if available
- Authors
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Arnav Tiwari, Shweta Pal, Neha Singh, Michael Leonard Anthony, Nilotpal Chowdhury, Shalinee RaoList of authors in order
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https://doi.org/10.4103/jme.jme_36_20Publisher landing page
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.4103/jme.jme_36_20Direct OA link when available
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Platelet, Medicine, Flagging, Context (archaeology), Peripheral blood, Analyser, Statistics, Internal medicine, Mathematics, Chemistry, Biology, Chromatography, Archaeology, Paleontology, HistoryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Medknow, Wolters Kluwer |
| primary_location.license | cc-by-nc-sa |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-sa |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Medical Evidence |
| primary_location.landing_page_url | https://doi.org/10.4103/jme.jme_36_20 |
| publication_date | 2022-05-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W6722311446, https://openalex.org/W2332116912, https://openalex.org/W2312675343, https://openalex.org/W6684625569, https://openalex.org/W6777077951, https://openalex.org/W1909851795, https://openalex.org/W6635532976, https://openalex.org/W6601097927, https://openalex.org/W2142178373, https://openalex.org/W1971195194, https://openalex.org/W2483561352, https://openalex.org/W1593119700, https://openalex.org/W26537171, https://openalex.org/W3022793493, https://openalex.org/W2167630753 |
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