Farmacovigilância em tuberculose: relato de uma experiência no Brasil
Bibliographic record
Abstract
Tuberculosis (TB) treatment frequently causes adverse reactions, because on one hand, it employs at least four drugs and on the other hand, these drugs are often used in association with other drugs, such as antiretroviral and glucose-lowering drugs, that interact with antitubercular agents. The Brazilian National Tuberculosis Control Program and the National Health Surveillance Agency (ANVISA) developed a partnership to implement a pilot pharmacovigilance project to encourage the reporting of adverse reactions to antitubercular agents. Training followed by monitoring visits was conducted by three reference health services for TB treatment. Among the bottlenecks identified, we found limitations in access to the information system (NOTIVISA), slow Internet connection, poor adverse event reporting in medical records, lack of multidisciplinary integration and involvement of managers, and fragility of information flows. As a consequence, technical instructional materials were developed, the NOTIVISA form was improved and shortened, indicators for monitoring notifications were proposed, and information flows were reset. We conclude that the partnership was successful and suggest a similar strategy for other programs. Integration of health teams as well as development of simplified notification tools are challenges to be overcome if pharmacovigilance actions are to be sustainable in the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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; both teacher heads agree on what is shown here.
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".