Helping Patients Follow Prescribed Treatment
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
MEDICAL RESEARCH DURing the past few decades has produced efficacious treatments for many health care disorders and, increasingly, these treatments can be selfadministered. Unfortunately, low adherence can undermine the effectiveness of care at many steps in the process. For example, 49% of patients who demonstrated elevated blood pressure on community screening failed to follow through with a referral for follow-up assessment. Of those who enter the medical care system, more than a third may drop out, especially during the first few months. While in care, the average consumption of medication has been found to be about 50%, with a very wide range from none to substantially more than 100%. Compliance with instructions to lose weight or stop smoking is substantially lower, with long-term success rates less than 10%. One of the important difficulties in managing low adherence is lack of accurate and affordable measures. Clinicians must frequently rely on their own judgment but unfortunately demonstrate no better than chance accuracy in predicting the adherence of their patients, even among patients for whom they feel confident about their predictions. A pragmatic approach to measuring adherence is presented in BOX 1. Based on a systematic review of studies adherence measures, asking nonresponders about their adherence will detect more than 50% of those with low adherence, with a specificity of 87%. Even when patients indicate that they have not taken all their medications as prescribed, their estimates usually substantially overestimate their actual adherence. Thus, the key validated question is “Have you missed any pills in the past week?” and any indication of having missed 1 or more pills signals a problem with low adherence. Overestimation of adherence by patients is difficult to study and is presently poorly documented. Reasons for overestimation could include difficulty recalling the details of medication taking, attempting to please practitioners or to avoid confrontation, or a combination of these factors. Other practical measures to assess adherence include watching for those who do not respond to increments in treatment intensity and patients who fail to attend appointments. More objective measures of compliance can also be of use when available. For example, drug levels in body fluids (blood, saliva, urine) can help in assessing patient compliance (eg, serum digoxin levels and levels of anti-
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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) | 0,003 | 0,003 |
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