Practical tips for surgical research: how to optimize patient recruitment.
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Notice bibliographique
Résumé
One of the most common challenges of randomized controlled trials (RCTs), both published and unpublished, is related to problems with recruitment. Investigators’ enthusiasm for ambitious recruitment in a trial often dissipates quickly with the realization that ambitious recruitment is often misguided. This common error has been dubbed “Lasagna’s Law”1 and Muench’s Third Law.2 Both laws point to the same principle: investigators greatly overestimate the pool of available patients who meet the inclusion criteria.3 Insufficient or untimely patient recruitment into RCTs has serious consequences. The length of the trial may need to be extended, leading to increased resource use and costs. Lengthy trials delay the availability of potentially beneficial treatments to the public.4 The integrity and validity of the study also rely on an adequate sample size. If the sample size is not achieved, there is an increased chance of committing a type II error (e.g., you are more likely to find no difference between treatments when one actually exists). The trial may have to be abandoned, and the results may not be publishable. The recruitment rate is influenced by both patient and investigator factors. A recent systematic review by Abraham and colleagues5 identified reasons why eligible patients may not want to participate in real or hypothetical surgical RCTs. Surgeons were also asked why they did not want to enroll eligible patients into real or hypothetical surgical trials. The top reasons for patient nonentry were that the patient had a preference for a certain therapy, he or she did not understand the trial (trial too complex), the patient did not want to be randomly assigned to a treatment and he or she feared a negative outcome or receiving a treatment that he or she felt was inferior. Investigators had similar reasons for not entering eligible patients, including difficulty following the study protocol (trial too complex) and completing the follow-up requirements, preference for a certain therapy and difficulties obtaining informed consent from patients. Understanding and addressing potential patient and investigator concerns is important when developing a recruitment strategy. In this article, we discuss the common issues encountered in recruiting patients for surgical trials. It is intended for anyone conducting surgical trials, including medical students, residents, and junior and senior researchers. By the end of this article, readers will be able to develop strategies to avoid some of the common pitfalls in recruitment and, if these difficulties occur, to rectify them.
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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 | 0,007 | 0,201 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle