Panorama nacional e internacional sobre dados e evidências de mundo real
Notice bibliographique
Résumé
In health regulation, evidence is needed to support the approval and the monitoring of several products of interest to public health, including medical devices.Attention has been drawn to a type of evidence known as real-world evidence, which is obtained from different data understood as real-world data, such as those generated by administrative systems of health services and health plans or recorded by the patient, including applications on cell phones or wearable devices.In recent years, international regulatory agencies have been exploiting the use of this evidence in various regulatory stages of medical devices´ life cycle.However, Brazil still does not have a scientific or regulatory framework that addresses this topic in depth.This work aimed to analyze the regulatory framework regarding the use of real-world evidence in the medical devices scenario, aiming to contribute to the improvement of the Brazilian regulatory system.To this end, a document analysis was carried out from documents issued by government agencies and health institutions from countries of relevance regarding the regulatory field of medical devices or their health systems, including Brazil, Canada, China, United States of America, Japan, United Kingdom United Kingdom, and European Union.Additionally, an event was conducted in the form of a workshop, which disseminated and discussed in Anvisa the knowledge obtained from this research.As a result, the following topics were mapped: the main concepts related to the theme; the potentialities and limitations inherent to this type of evidence; the main uses of real-world data and real-world evidence in medical device regulation; the requirements related to the suitability of these data for regulatory purposes and, lastly, the international and national regulatory landscape related to the topic.It was identified that real-world evidence has been used in several stages of medical devices´ life cycle, such as its use for the regularization of innovative products and those intended for rare diseases.Concerns and challenges include real-world data quality, methodological transparency, and ethical aspects.It was found that Brazil is in incipient stage when compared to the international scenario studied.Based on the knowledge structured from the document analysis, opportunities regarding the use of real-world evidence in the field of medical devices were identified in order to improve the Brazilian regulatory system.The opportunities were organized in the following axes: establishment of the subject as a strategic pillar; preparation of guidance documents; improvement of the national regulatory framework, such as the adoption of the conditional approval regime and the improvement of the current surveillance system; improvement and use of existing information systems to obtain data on the safety and effectiveness of medical devices used by SUS users; joint work with stakeholders and qualification of Anvisa´s technical staff and other instances of SUS.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,091 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,009 | 0,012 |
| Études des sciences et des technologies | 0,004 | 0,019 |
| Communication savante | 0,017 | 0,013 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,008 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».