Trends in Publication Outcomes of Platform Presentations at National Association of Medical Examiners (NAME) Annual Meetings From 2013 to 2022
Bibliographic record
Abstract
The NAME annual meeting focuses on platform presentations as a means of sharing research, ideas, and education; however, little is known about the rate at which these presentations are subsequently published. This study analyzed trends related to publication outcomes of 651 presentations given from 2013 to 2022. Using Python scripts that queried the PubMed database, we found that 175 presentations (27%) went on to be published in the peer-reviewed literature [we also accounted for presentations published in Academic Forensic Pathology (AFP) in the years when AFP was not indexed in PubMed]. The journals AFP and the AJFMP accounted for most of the publications, but over 30 other journals were identified. Publications in AFP declined in the latter years of our study range, while publications in AJFMP increased. First authors from NMS, the CDC, and Ottawa-based institutions were among the most likely to publish their presentations. To assess the performance of our automated scripts, we also performed a manual Internet/PubMed search of presentation titles and authors; our automated scripts detected 97% of all matched presentations with PubMed. Information gleaned from studying bibliometric trends can help inform strategies for improving future educational programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".