Clinical utility of overviews on adverse events of pharmacological interventions
Notice bibliographique
Résumé
BACKGROUND: Overviews (i.e., systematic reviews of systematic reviews, meta-reviews, umbrella reviews) are a relatively new type of evidence synthesis. Among others, one reason to conduct an overview is to investigate adverse events (AEs) associated with a healthcare intervention. Overviews aim to provide easily accessible information for healthcare decision-makers including clinicians. We aimed to evaluate the clinical utility of overviews investigating AEs. METHODS: We used a sample of 27 overviews exclusively investigating drug-related adverse events published until 2021 identified in a prior project. We defined clinical utility as the extent to which overviews are perceived to be useful in clinical practice. Each included overview was assigned to one of seven pharmacological experts with expertise on the topic of the overview. The clinical utility and value of these overviews were determined using a self-developed assessment tool. This included four open-ended questions and a ranking of three clinical utility statements completed by clinicians. We calculated frequencies for the ranked clinical utility statements and coded the answers to the open-ended questions using an inductive approach. RESULTS: The overall agreement with the provided statements was high. According to the assessments, 67% of the included overviews generated new knowledge. In 93% of the assessments, the overviews were found to add value to the existing literature. The overviews were rated as more useful than the individual included systematic reviews (SRs) in 85% of the assessments. The answers to the open-ended questions revealed two key aspects of clinical utility in the included overviews. Firstly, it was considered useful that they provide a summary of available evidence (e.g., along with additional assessments, or across different populations, or in different settings that have not been evaluated together in the included SRs). Secondly, it was found useful if overviews conducted a new meta-analysis to answer specific research questions that had not been answered previously. CONCLUSIONS: Overviews on drug-related AEs are considered valuable for clinical practice by clinicians. They can make available evidence on AEs more accessible and provide a comprehensive view of available evidence. As the role of overviews evolves, investigations such as this can identify areas of value.
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.
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 | 0,432 | 0,174 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,015 | 0,014 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,020 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».