Reply: A Systematic Review of Surgical Randomized Controlled Trials: Part I. Risk of Bias and Outcomes: Common Pitfalls Plastic Surgeons Can Overcome, and A Systematic Review of Surgical Randomized Controlled Trials: Part 2. Funding Source, Conflict of Interest, and Sample Size in Plastic Surgery
Notice bibliographique
Résumé
Sir: We enjoyed reading the letter by Drs. Gardiner and Jain regarding our systematic review1,2 and appreciate the opportunity from the Editor to respond. Gardiner and Jain identify a barrier of culture that affects the quality and quantity of randomized controlled trials in plastic surgery. They propose multicenter trainee collaboration as a component of a long-term solution. Among all forms of clinical studies, randomized controlled trials are considered the gold standard for comparing therapeutic interventions when a state of equipoise exists. When comparing surgical procedures, unique challenges are encountered in the conduct of surgical randomized controlled trials that are not present in randomized controlled trials comparing medical interventions (e.g., the cultural resistance to randomization, the challenge of blinding, variability in surgeon skill, and surgical learning curves). We believe the greatest challenge of plastic surgery randomized controlled trials that compare surgical interventions involves recruitment. Problems may be identified at protocol, site, surgeon, and patient levels. Recruitment issues can exist in trial design, where the protocol causes inherent difficulties. Conversely, issues may be specific to individual sites in multicenter trials, where those who are noncompliant or noncommitted limit recruitment. More difficult to address are issues with specific surgeons, where despite agreeing to participate in the trial, they may lack the resources, time, or motivation to fulfill protocol requirements. Enthusiasm to join the trial often disappears with recognition of the effort needed to practically recruit patients; this is known as the Lasagna law.3 Finally, patient-related recruitment issues may exist, where trials require frequent or inconvenient follow-up with limited incentive. The practice of surgery is entrenched in a culture of “treating patients with practices based on rigidly held protocols learned in residency training or opinions presented by leaders in the field.”4 The training and experience needed to become highly skilled tend to create surgeons who favor a certain surgical approach to treat a specific problem. A surgeon’s preference for one surgical technique can make it difficult to convince them to participate in a randomized controlled trials comparing surgical interventions. Furthermore, it may be inappropriate to require a surgeon to perform both interventions; the act of random allocation may in fact reduce the effectiveness of the intervention. We read with interest the initiative Gardiner and Jain describe in the United Kingdom for the creation of a trials network. Large clinical trials networks provide an opportunity to increase the rate of patient enrollment and to increase the generalizability of a randomized controlled trial’s results.5 Clinical trials networks can help to address protocol-related recruitment issues, and potentially patient-related issues, as an individual site may not have the patient volume to complete a large randomized controlled trial. A second opportunity for design and conduct of randomized controlled trials in plastic surgery involves “expertise-based” randomized controlled trial designs, in which the patient is randomized to a surgeon/group of surgeons (experts) committed to performing an intervention.6 With this design, bias from a surgeon’s preference for one surgical technique, which can manifest as differential procedural performance, co-interventions, and subjective outcome assessment is negated. As in conventional randomized controlled trials, plastic surgeons in expertise-based trials cannot be blinded; however, in expertise-based randomized controlled trials, the risk of detection bias is minimized. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Sophocles H. Voineskos, M.D., M.Sc.Christopher J. Coroneos, M.D., M.Sc.Division of Plastic and Reconstructive SurgeryDepartment of Surgery, andSurgical Outcomes Research Centre Achilleas Thoma, M.D., M.Sc.Division of Plastic and Reconstructive SurgeryDepartment of SurgerySurgical Outcomes Research Centre, andDepartment of Clinical Epidemiology and Biostatistics Mohit Bhandari, M.D., Ph.D.Department of Clinical Epidemiology and Biostatistics, andDivision of Orthopaedic SurgeryDepartment of SurgeryMcMaster UniversityHamilton, Ontario, Canada
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 | 0,067 | 0,371 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,006 | 0,011 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,043 | 0,038 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».