Bibliographic record
Abstract
Background: Authorship is a pinnacle activity in academic medicine that often involves collaboration and a mentor–mentee relationship. The International Committee of Medical Journal Editors criteria for authorship (ICMJEc) are intended to prevent abuses of authorship and are used by more than 5500 medical journals. However, the binary ICMJEc have not yet been quantified. Aim: To develop a numeric scoring rubric for the ICMJEc to corroborate the authenticity of authorship claims. Methods: The four ICMJEc were separated into the nine authorship components of conception, design, data acquisition, data analysis, interpretation of data, draft, revision, final approval and accountability. In spring 2021, members of an international association of medical editors rated the importance of each authorship component using an 11-point Likert scale ranging from 0 (no importance) to 10 (most important). The median component scores were used to calibrate the pairwise comparisons in an analytic hierarchy process (AHP). The AHP priority weights were multiplied against a four-level perceived effort/capability grade to calculate an authorship score. Results: Sixty-six decision-making medical editors completed the survey. The components had the median scores/AHP weights: conception 7.5/5.3%; design 8/8.9%; data acquisition 7/3.6%; data analysis 7/3.6%; interpretation of data 8/8.9%; draft 8/8.9%; revision 8/8.9%; final approval 9/20.1%; and accountability 10/31.8%, with Kruskal–Wallis Chi 2 = 65.11, p < 0.001. Conclusion: The editors rated accountability as the most important component of authorship, followed by the final approval of the manuscript; data acquisition had the lowest median importance score for authorship. The scoring rubric ( https://tinyurl.com/eyu86y96 ) transforms the binary tetrad ICMJEc into 9 quantifiable components of authorship, providing a transparent method to objectively assess authorship contributions, determine authorship order and potentially decrease the abuse of authorship. If desired, individual journals can survey their editorial boards and use the AHP method to derive customized weightings for an ICMJEc-based authorship index. Keywords: authorship, ICMJE, academic medicine, ethics, medical editors, analytic hierarchy process, survey
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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 | 0.071 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".