Reply to Letter to Editor: Orthopaedic Surgeons Prefer to Participate in Expertise-based Randomized Trials
Notice bibliographique
Résumé
We thank Dr. Biau and Professor Porcher for their comments regarding our recently published article, “Orthopaedic Surgeons Prefer to Participate in Expertise-based Randomized Trials” [1]. We wish to first emphasize we do not view the expertise-based design as a panacea for all challenges related to surgical trials. We do, however, view the expertise-based design as an option that holds substantial promise, under circumstances outlined in our paper, to provide less biased results than the conventional design and to enhance feasibility of conducting randomized controlled trials (RCTs) in surgery. Drs. Biau and Porcher acknowledge differential expertise bias will affect the results of a conventional RCT, but they feel assured statistics can resolve the problem. To avoid confounding and assure optimal statistical adjustment, however, researchers first must establish all of the predictors of outcome to determine what effect is attributed to the surgeons, and then each surgeon must provide a sufficient number of cases in a large multicenter study to determine whether an individual surgeon was contributing an effect. Given that these circumstances are uncommon, we are less confident statistics can resolve the problem. Regarding the simulation described by Drs. Biau and Porcher, it is difficult to know exactly what it shows given the limited information provided. Further, it is uncertain whether the differences between the expertise-based RCT results and the adjusted conventional RCT results, which assume the researchers know all of the points specified above, are clinically important in terms of patient-important outcomes. Regarding the second issue questioning the applicability of the results of an expertise-based RCT, we encourage consideration of the paper written by Dr. Devereaux and colleagues and published in the British Medical Journal [2]. Briefly, in that paper, the authors described the difference between explanatory and pragmatic approaches to clinical trials emphasizing the expertise-based trial can be explanatory (limited to only surgeons with advanced expertise in ideal clinical settings) or pragmatic (including surgeons with at least basic competence in routine clinical settings). The applicability of the results of an expertise-based trial would relate specifically to this feature of the design. Thus, researchers should clearly describe the criteria for defining expertise. This issue of applicability is the same for conventional trials. Regarding Dr. Biau and Professor Porcher's final point about the feasibility of conducting expertise-based trials, we have knowledge of at least one national multicenter expertise-based trial investigating the effectiveness of an orthopaedic intervention in the shoulder. At one center, patients referred to any of the participating surgeons are seen in a clinic that is run by the fellows to determine eligibility, and if eligible, to describe the study including the study design. In another center, a similar sort of setup is run by a primary care physician with expertise in sports medicine. Approximately 5% of patients screened have refused for the reasons suggested by Drs. Biau and Porcher. It is unknown how many of these patients would have proved eligible for the trial; we can only report that approximately 50% of patients are found ineligible at the time of surgery (usually because of concomitant disease that precludes participation). Thus, the magnitude of the loss of potential participants may be much less. Time will tell whether the experience of researchers participating in other trials is similar to our experience.
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 | 0,023 | 0,161 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,042 | 0,046 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,010 |
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 ».