Depicting oncologists' perceptions and knowledge of global disparities in conflicts of interest reporting: The ONCOTRUST-1 study.
Notice bibliographique
Résumé
9002 Background: Conflicts of interest (COI) between oncologists and the pharmaceutical industry might considerably influence how the presentation of the research results is delivered, impacting treatment decisions, and policy-making.While there are regulations on reporting COI in high-income countries (HICs), little is known about their reporting in low- and middle-income countries (LMICs).ONCOlogy TRansparency Under Scrutiny and Tracking (ONCOTRUST-1) is a pilot global survey to explore the knowledge and perceptions of oncologists regarding COI. Methods: We designed an online 27-question-based survey in English language to explore the perceptions and knowledge of oncologists regarding COI, with an emphasis on LMICs.Illustrative examples of COI were proposed, based on definitions from the American Society of Clinical Oncology (ASCO) and published literature. Descriptive statistics and the CROSS guidelines were used to report the findings. Results: ONCOTRUST-1 surveyed 200 oncologists, 70.9% of them practicing in LMICs. Median age of the respondents was 36 (range: 26-84) years; 47.5% of them were women. The median number of years of clinical practice was 9 (range: 1-51). 40.5% of respondents reported weekly visits by pharmaceutical representatives to their institutions. Regarding oncologists’ perceptions of COI that require disclosure, direct financial benefits, such as honoraria ranked highest (58.5%), followed by gifts from pharmaceutical representatives (50%) and support for attending conferences (44.5%). In contrast, personal or institutional research funding, sample drugs, consulting or advisory board, expert testimony, and food and beverage funded by pharmaceutical industry were less frequently considered as COI. Moreover, only 24% of surveyed oncologists could correctly categorize all situations representing a COI. Regarding support received from industry, 51.5% of respondents acknowledged trips to conferences as the most common form of support, followed by sample drugs (20.5%). Despite recognizing these interactions, 15% of respondents admitted feeling pressured to prescribe specific drugs due to their COI. Regarding COI reporting, a notable portion of participants indicated they report COI in their presentations (59%) or when publishing their research (30%). The presence of local regulations to manage COI were reported by 35.5% of respondents. The majority advocated for clearer policies and regulations (65%), training and education (63.5%), and an open COI database (55%) to improve COI reporting in oncology. Conclusions: These findings underscore the importance of clear guidelines, education, and transparency in reporting COI in oncology. This hypothesis-generating pilot survey provided the rationale for ONCOTRUST-2 study which will compare perceptions of COI among oncologists in LMICs and HICs.
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 | 0,006 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».