Analysis of ASCO clinical guidelines authorship and institutional representation using HemOnc.org.
Bibliographic record
Abstract
e13577 Background: Evidence-based clinical practice guidelines (CPGs) are vital for safe and updated oncology care. ASCO produces CPGs across solid and blood cancers and supportive care. Guideline authors include volunteers and ASCO staff. This study characterizes ASCO CPGs by authorship and their affiliations. Methods: ASCO-only and collaborative CPGs published in peer-reviewed journals, excluding rapid recommendations, were analyzed. CPGs were categorized into 16 clinical groups. Author and institutional data were extracted from PubMed and normalized using Python and manual abstraction. Authors’ gender was determined using gender-guesser, genderAPI, and manual review. Statistical comparisons used Fisher’s exact test. Data were sourced from HemOnc.org on 12/02/2024. Results: We identified 146 eligible CPGs (1999–2024), with 1297 unique authors. 102 (8%) were ever-first authors and 108 (8%) were ever-last (senior) authors. The most prolific first author was in this role six times (breast/classical hematology), while the most prolific last author, five times (breast cancer). A total of 482 institutions were represented, and senior authorship spanned 59 institutions, with 33% affiliated with five institutions: University of Michigan, MD Anderson, Dana-Farber, Johns Hopkins, and ASCO. Only four CPGs had first and senior authors from the same institution. 844 (65%) authors were USA-based. Canada, UK, Italy, Japan, and Netherlands were sequentially the next five highest contributors (142 authors). Top groups included breast (36 CPGs), gastrointestinal (21), and supportive care (20). Immunotoxicity CPGs had the highest collaboration (31 authors/guideline) compared to the lowest in radiotherapy toxicity (11.7 authors/guideline). Over the 25-year publication period, 18 authors transitioned from first to senior authorship, and 48 different authors transitioned from middle to first or senior authorship. Based on algorithmically assigned gender, 57% of all authors, 57% of first authors, and 60% of senior authors were men. Immunotoxicity had the highest representation of women (60%), while radiotherapy toxicity had the lowest (10.5%). Of the 1297 authors, 283 (22%) also contributed to pivotal clinical trials publications supporting regulatory approvals. These authors were more likely to be men (70% men vs 30% women) than the non-contributing authors (53% men vs 47% women) (OR 2.02, 95% CI 1.52–2.68). Conclusions: ASCO CPGs demonstrate broad institutional and international representation, though senior author concentration in a smaller number of institutions was observed. We show gender differences exist in ASCO guideline authorship, although they are significantly less prominent overall than in the subgroup of pivotal clinical trial authors. Future research should explore whether ASCO guideline authorship diversity reflects the demographics of oncology subfields.
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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 | 0.011 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.030 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".