Characterization of industry relationships in oncology.
Notice bibliographique
Résumé
11025 Background: Collaborative relationships between academic oncology and the pharmaceutical industry are essential for therapeutic development in oncology. Despite this, formal training and mentorship in developing productive industry collaborations are not routinely included in oncology training. Since little research has been done to characterize and optimize the efficiency of these relationships, we sought to better understand the nature of such collaborations in order to identify areas for optimization. Methods: An electronic survey was administered to 1000 randomly selected ASCO members. The survey included 23 questions eliciting demographic and practice information, and 26 questions eliciting respondents’ views around oncology-industry collaborations. Survey results were analyzed using descriptive statistics. Results: There were 225 survey respondents. Most were from the United States (70%), worked at an academic institution (60.1%), worked in medical oncology (81.2%), and had an active relationship with industry (85.8%). 26.7 % of respondents reported difficulty establishing a relationship with industry collaborators. Many relied on federal (39.5%) or departmental (30.2%) funding to supplement their research ventures. Partnerships were initiated by the respondents themselves (34.6%) or industry partners (31.9%) with similar frequency, whereas institutional affiliations (15.7%) and collaborative groups (5.8%) were reported as less common means for establishing collaborations. The majority (85.3%) of respondents stated these collaborations were of importance to their career. Inclusion in industry sponsored trials (71.1%) and commitment to research funding (66.3%) were considered early signs of a productive relationship, whereas lack of effective communication (86.1%) or little engagement by senior industry leadership (63.1%) were early red flags. Most respondents (75%) did not report having had mentorship in developing these relationships. Scientific integrity was generally thought to be preserved (92%) and there was little concern over the quality of the collaborative product (95%). Many shared concern over potential conflict of interest if a compensated relationship promoted an industry product for clinical care/research (60%), yet also stated these relationships did not shape their interactions with patients (67%). Conclusions: This study provides novel data characterizing the nature of collaborative industry-academia relationships in oncology. While respondents considered these collaborations an important part of clinical and academic oncology, formal education or mentorship around these relationships is rare. Further study exploring the structure of effective industry collaborations, optimizing methods to provide education in this area at all career stages, navigating conflict of interest issues in these relationships, and understanding industry perspectives is warranted.
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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 | 0,008 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».