Determinants of patients' treatment preferences in a clinical trial
Notice bibliographique
Résumé
Several researchers have suggested that patients' preferences for a particular form of treatment should be taken into account in clinical trials. Preferences may influence the outcome of treatment, especially in trials when patients cannot be blinded to the type of treatment received and the outcome is based on patients' evaluations of therapy. Participants in this study were 136 edentulous patients who took part in a randomised controlled clinical trial comparing two types of treatments for edentulism: conventional dentures and implant-supported prostheses. Prior to receiving treatment, subjects were required to complete a questionnaire regarding their satisfaction with their present prostheses. In addition, they were asked to indicate which treatment they would prefer if given a choice. The objective of this study was to determine whether there are important differences among study participants between patients who have a treatment preference and those who do not. The effects of satisfaction with pre-treatment prostheses, age, gender and level of education on preferences were examined. Level of satisfaction with the original dentures and level of education were significant predictors of preference. Compared to subjects who rated their satisfaction with their current condition as 'low', the odds ratios associated with having a preference for implant treatment were 0.31 (95% CI: 0.09 to 0.96) for subjects who rated their prostheses in the 'medium' range and 0.11 (95% CI: 0.03 to 0.41) for those who rated in the 'high' range. In addition, subjects with high levels of education were significantly less likely to have a preference for either conventional or implant treatments (OR = 0.18, 95% CI: 0.02 to 0.77 and OR = 0.20, 95% CI: 0.05 to 0.76, respectively) compared to those with low education. Neither age nor gender was a significant predictor of preference. We suggest that study designs which incorporate patients' preferences must take into account possible differences between preference groups that might confound the relationship between preference and the outcome of interest.
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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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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 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 ».