Panorama nacional e internacional sobre dados e evidências de mundo real
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".