Underrecognition of Pathologist Contributions to Articles Published in a Major Multidisciplinary Medical Journal
Bibliographic record
Abstract
The Canadian Medical Association Journal (CMAJ) is a high-impact multidisciplinary medical journal. We have observed instances in which a pathology diagnosis, documented with gross or microscopic images, forms an integral part of a CMAJ article, but a pathologist is neither an author nor acknowledged as a contributor. To examine the hypothesis that pathologist contributions are underrecognized and/or underdocumented, we reviewed all CMAJ articles over a 6-year period (September 2003-2009), and correlated the use of pathology images with pathologist authorship or contribution. For each article containing pathology images, department affiliations of authors were determined, and acknowledgments were assessed. Although only 1.7% of articles contained pathology images, 47% (26/55) of these articles did not include a pathologist as either an author or a contributor. We conclude that important intellectual contributions of pathologists are underrecognized and suggest that the scientific credibility of pathology data is in doubt when pathologists do not take on full responsibility of authorship or are not acknowledged as contributors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".