PD21 Data Sources And Real-World Data On Medical Devices In The Brazilian Scenario
Bibliographic record
Abstract
Introduction The Brazilian government has made efforts in systems to generate data from medical devices (MD). This work explores the main systems and data sources in the perspective of contributing as a source to generate real world data (RDW). Methods Document review of relevant national data sources for MD. In addition, a structured search was carried out in EMBASE using key descriptors for RWD applied to the regulatory context and to the management of health technologies, without date or language restrictions. Results Eighteen primary federal government data sources for MD were identified. Not all sources are publicly accessible. Of the articles, the search returned 1,185 results, of which 29 titles were selected and 8 met the protocol’s objective. Included articles were from Europe, the United States and Canada. As in other countries, Brazil initially systematized DM administrative data to meet commercial and financial demands. With the evolution of health technology assessment methods, the use of RDW has become imperative to assess the value of MD to society. Common examples from these countries are implantable MD databases. Current challenges focus on data linkage and quality, in addition to standardized naming. The adoption of the Unique Device Identification (UDI) is one of the promising initiatives to facilitate traceability throughout the lifecycle proposed in the International Medical Device Regulators Forum (IMDRF) of which Brazil is a member. Among the systems, the following stand out: i) ConectSUS, which intends to provide access to health information centered on the patient, anywhere and at any time; ii) National implant registry that generates data on implanted prostheses and stents, surgical techniques used, the profile of patients and the health services involved. Conclusions This work showed the similarities between Brazil and other countries in the management of MD data throughout its life cycle, as well as mapped the national primary data sources for MD.
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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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.052 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".