Toward Larger, More Definitive Trials: A North American Trainee-Led Research Collaborative
Notice bibliographique
Résumé
Sir: High-quality randomized controlled trials are lacking in the plastic surgery literature, as indicated by Voineskos et al. in their recent publications: “A Systematic Review of Surgical Randomized Controlled Trials: Part I. Risk of Bias and Outcomes: Common Pitfalls Plastic Surgeons Can Overcome”1 and “A Systematic Review of Surgical Randomized Controlled Trials: Part 2. Funding Source, Conflict of Interest, and Sample Size in Plastic Surgery.”2 As our Canadian colleagues suggested, producing larger, more definitive trials in the field can be accomplished through increased engagement and collaboration.2 One method currently used to accomplish this goal in the United Kingdom is the implementation of trainee-led research collaboratives. The benefits of these networks include large-scale data collection and patient recruitment in a shorter period, decreased repetition, and greater generalizability of findings.3 At present, over 25 trainee-led research networks exist in the United Kingdom spanning multiple specialties. We have taken the lessons learned by our British colleagues to heart. Our team of plastic surgery trainees were the first to adopt the trainee-led research collaborative model in Canada and have been working nationally as a part of the Canadian Plastic Surgery Research Collaborative since September of 2015 (www.cansurg.org). This network has representation from all 13 Canadian academic plastic surgical training programs and their affiliated health centers. Our collaborative consists of over 30 resident physicians, medical students, and attending members. To date, we have completed one national project, and have three other multicenter studies ongoing (Fig. 1).4Fig. 1.: Implementing a multicenter study through a trainee-led research collaborative. ICMJE, International Committee of Medical Journal Editors.Since launching, our neurosurgery colleagues have also adopted this model and have established the Canadian Neurosurgery Research Collaborative. We anticipate that this model will continue to gain popularity as other specialties and countries adopt and implement trainee-led research networks. We hope that this may someday lead to the establishment of a North American collaborative as we work toward a common goal of improving the level of evidence in the plastic surgery literature. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Mona T. Al-Taha, B.B.A.Faculty of Medicine Sarah A. Al Youha, M.D. Osama Samargandi, M.D.Division of Plastic and Reconstructive SurgeryDalhousie UniversityHalifax, Nova Scotia, Canada Helene Retrouvey, M.D.C.M.Division of Plastic and Reconstructive SurgeryUniversity of TorontoToronto, Ontario, Canada Michael Bezuhly, F.R.C.S.C.Division of Plastic and Reconstructive SurgeryDalhousie UniversityHalifax, Nova Scotia, CanadaOn behalf of the Canadian Plastic Surgery ResearchCollaborative
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,589 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,005 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».