Analysis of ASCO clinical guidelines authorship and institutional representation using HemOnc.org.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,018 | 0,030 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».