Challenges of Pharmacovigilance in Brazil
Bibliographic record
Abstract
Pharmacovigilance encompasses the detection, evaluation, understanding, and prevention of adverse effects or any other drug-related problems. Knowledge of real and independent pharmacovigilance data is essential because clinical trials with medicinal products are limited and do not reveal all adverse effects of a product. The spontaneous notification system is one of the main tools used in pharmacovigilance. However, important remaining challenges for health professionals are the accurate recognition of adverse drug reactions and reporting routinely and systematically during their work. Once low notification rates make it harder to detect and monitor potential safety issues, it is needed risk assessment, and regulatory actions to safeguard patient safety. The objective of this study is to present the challenges of pharmacovigilance in Brazil. The implementation of computerized active search tools significantly improves the identification of possible adverse drug effects. Effective pharmacovigilance is crucial to ensure the safety and integrity of healthcare systems, to avoid lengthy hospital stays and to optimize healthcare spending. However, pharmacovigilance tools remain underused, undervalued, or even unknown in Brazil.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".