ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Automated Comparison of Pathology Reports for On-the-job Assessment of Residents.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- PARAFFIN: A software tool for Pathology Report Automated Feedback for Improved Education of anatomic pathology trainees.Journal of pathology informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The clinical competency of residents at teaching hospitals is always under scrutiny. Ideally, assessment should reflect competency on-the-job, under realistic circumstances, and include evaluating their medical reports. Currently, the assessment is done manually by the attending physicians, which adds to the cognitive load. In this study, we developed an automated system for assessing medical resident's pathology reports. Our system used natural language processing (NLP) techniques to identify different lexical and semantic similarity scores at sentence level as well as chunk level. We then used supervised learning to classify the reports into three categories- No Change (NC), Minor Changes (MiC), and major changes (MaC), reflecting how much the attending physician's report differs from that of the resident. Our system was able to classify the reports with an accuracy of 73.6%. Although moderately successful, our work shows the potential and future of automated assessment systems in the biomedical domain.
Identifiers
31259004PMC6568070What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.