Thalidomide surveillance and pharmacovigilance in Brazil – an overview
Bibliographic record
Abstract
Introduction:The thalidomide is probably the best-known teratogenic drug and still results in cases of severe physical deformities in children born in Brazil. Objective:To present the overall context of surveillance and pharmacovigilance of thalidomide in Brazil. Method:This article presents a narrative review of current literature concerning thalidomide regulation, policies, and pharmacovigilance in Brazil. Results:New cases of congenital abnormalities whose phenotype is compatible with thalidomide embryopathy were identified in the last ten years, while the approval of thalidomide for new indications was recently updated. The mechanisms of diagnosing thalidomide embryopathy are complex, remaining the challenge in distinguishing this condition from other congenital abnormalities. The increasing number of thalidomide users in Brazil is correlated with the occurrence of embryopathy and the real extension of the rationality of its use is largely unknown. Additionally, our pharmacovigilance and surveillance systems are predominantly based on voluntary reports, issues that remains over the years. Conclusions:The policies have improved over the years to prevent the fetus from being exposed to thalidomide, and current regulation establishes rules for controlling its distribution, prescription, dispensation, and use. Brazilian surveillance system is manual and pharmacovigilance is supported by voluntary reports. The failure of the system to properly control the thalidomide use and its effects might lead to serious consequences to the community; therefore, this subject deserves constant attention.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".