Bibliographic record
Abstract
To the Editor Franko et al1 raised an extremely important, longstanding, and ongoing predicament in their study, highlighting the underrecognition of pathologist contributions to articles published in a major multidisciplinary medical journal. In their study, Canadian Medical Association Journal (CMAJ) articles were scanned for the use of pathology images and correlated with the authors’ department affiliation. Using this design, they found that 47% of articles with a pathology image did not include a pathologist as either an author or a contributor. Increased awareness of these circumstances among laboratory medicine physicians who are the primary readers of the American Journal of Clinical Pathology is paramount. However, we believe that this study would have had a greater impact had it been published in a journal that caters to a multidisciplinary health sciences audience such as CMAJ. Unfortunately, we have serious concerns that the study design may have substantially underestimated the extent of pathologists’ underrecognition. The contribution of pathologists to scholarly scientific work reaches far beyond the contribution of an image. Many studies use laboratory information systems to identify their cohort or require pathology review to confirm diagnosis and achieve consistency based on preset criteria. Moreover, a pathology review is often carried out to complete data collection of parameters that are not necessarily addressed or routinely reported clinically. This process represents a substantial contribution to acquisition or analysis and interpretation of data, therefore qualifying as an effort that deserves authorship based on the criteria suggested by the International Committee of Medical Journal Editors.2 Pathology review contributes to the quality of the work, and including pathologists as authors not only serves as an acknowledgment of their scholarly contribution but also underscores their responsibility for providing accurate pathology-derived data. It gives credibility to multidisciplinary publications and validates its pathology-based data.
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 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 | 0.012 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".