Patient Preference-based Treatment Thresholds and Recommendations
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Notice bibliographique
Résumé
BACKGROUND: Decision analysis (DA) and the probability-tradeoff technique (PTOT) are patient preference-based methods of determining optimal therapy for individuals. Using aspirin therapy for the primary prevention of stroke and myocardial infarction (MI) in elderly persons as an example, the objective of this study was to determine whether group-level treatment thresholds and individual-level treatment recommendations derived using PTOT are identical to those of DA incorporating the patients' own values. METHODS: Persons in a pilot study of the efficacy of aspirin in the prevention of stroke and MI were asked to participate. Participant values and utilities for pertinent health states (e.g., minor and major stroke, MI, major bleeding episode) were determined. Then, in three hypothetical clinical situations in which the chance of stroke or MI was varied, PTOT was used to directly determine treatment thresholds for aspirin therapy (i.e., the smallest reduction in MI or stroke risk for which participants would be willing to take aspirin). Using DA modeling, with the same probabilities of events as in the PTOT exercise and incorporating participants' own values, treatment thresholds for the three clinical situations were determined. The thresholds determined by the two approaches were compared. Finally, based on these treatment thresholds, using the best estimates of the efficacy of aspirin to prevent first-time stroke and MI, PTOT and DA treatment recommendations for individual participants were compared. RESULTS: The 42 participants reported that a major stroke was the least desirable health state, followed by MI, minor stroke, and major bleeding. The minimum risk reduction required to take aspirin was greater for MI prevention compared with stroke prevention. For the two clinical situations in which the hypothetical efficacy of aspirin to prevent stroke was varied, treatment thresholds for the PTOT versus DA approaches differed (p < 0.04), but this difference was not significant (p = 0.19) for the MI-based clinical situation. Using the best estimate of the efficacy of aspirin to prevent first-time stroke and MI, PTOT and DA treatment recommendations whether or not to take aspirin were discordant for 38% of participants (16 of 42) (p < 0.001). CONCLUSIONS: Patient preference-based group-level treatment thresholds and individual-level treatment recommendations can differ significantly depending on whether PTOT or DA is used, apparently because the two emphasize different aspects of the decision-making process. DA theory assumes that effective therapeutic decision making should maximize both quality and quantity of life; with PTOT, the emphasis for effective clinical decision making allows patients to be fully engaged in the process, thus hopefully leading to fully informed decisions that may result in satisfaction and compliance.
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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 | 0,000 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 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