International lessons in new methods for grading and integrating cost effectiveness evidence into clinical practice guidelines
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
Economic evidence is influential in health technology assessment world-wide. Clinical Practice Guidelines (CPG) can enable economists to include economic information on health care provision. Application of economic evidence in CPGs, and its integration into clinical practice and national decision making is hampered by objections from professions, paucity of economic evidence or lack of policy commitment. The use of state-of-art economic methodologies will improve this. Economic evidence can be graded by 'checklists' to establish the best evidence for decision making given methodological rigor. New economic evaluation checklists, Multi-Criteria Decision Analyses (MCDA) and other decision criteria enable health economists to impact on decision making world-wide. We analyse the methodologies for integrating economic evidence into CPG agencies globally, including the Agency of Health Research and Quality (AHRQ) in the USA, National Health and Medical Research Council (NHMRC) and Australian political reforms. The Guidelines and Economists Network International (GENI) Board members from Australia, UK, Canada and Denmark presented the findings at the conference of the International Health Economists Association (IHEA) and we report conclusions and developments since. The Consolidated Guidelines for the Reporting of Economic Evaluations (CHEERS) 24 item check list can be used by AHRQ, NHMRC, other CPG and health organisations, in conjunction with the Drummond ten-point check list and a questionnaire that scores that checklist for grading studies, when assessing economic evidence. Cost-effectiveness Analysis (CEA) thresholds, opportunity cost and willingness-to-pay (WTP) are crucial issues for decision rules in CEA generally, including end-of-life therapies. Limitations of inter-rater reliability in checklists can be addressed by including more than one assessor to reach a consensus, especially when impacting on treatment decisions. We identify priority areas to generate economic evidence for CPGs by NHMRC, AHRQ, and other agencies. The evidence may cover demand for care issues such as involved time, logistics, innovation price, price sensitivity, substitutes and complements, WTP, absenteeism and presentism. Supply issues may include economies of scale, efficiency changes, and return on investment. Involved equity and efficiency measures may include cost-of-illness, disease burden, quality-of-life, budget impact, cost-effective ratios, net benefits and disparities in access and outcomes. Priority setting remains essential and trade-off decisions between policy criteria can be based on MCDA, both in evidence based clinical medicine and in health planning.
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.
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 | 0,088 | 0,151 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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