Incidence of Serious Side Effects from First-Line Antituberculosis Drugs among Patients Treated for Active Tuberculosis
Bibliographic record
Abstract
Major adverse reactions to antituberculosis drugs can cause significant morbidity, and compromise treatment regimens for tuberculosis (TB). Among patients treated for active TB we estimated the incidence, and risk factors, of major side effects from first-line anti-TB drugs. Side effects, resulting in modification or discontinuation of therapy, or hospitalization, were attributed on the basis of resolution after withdrawal, and/or recurrence with rechallenge. Among 430 patients treated between 1990 and 1999, the incidence of all major adverse effects was 1.48 per 100 person-months of exposure (95% confidence interval [95% CI], 1.31 to 1.61) for pyrazinamide, compared with 0.49 (95% CI, 0.42 to 0.55) for isoniazid, 0.43 (95% CI, 0.37 to 0.49) for rifampin, and 0.07 (95% CI, 0.04 to 0.10) for ethambutol. Occurrence of any major side effect was associated with female sex (adjusted hazard ratio, 2.5; 95% CI, 1.3 to 4.7), age over 60 years (adjusted hazard ratio, 2.9; 95% CI, 1.3 to 6.3), birthplace in Asia (adjusted hazard ratio, 2.5; 95% CI, 1.3 to 5.0), and human immunodeficiency virus-positive status (adjusted hazard ratio, 3.8; 95% CI, 1.05 to 13.4). Pyrazinamide-associated adverse events were associated with age over 60 years (adjusted hazard ratio, 2.6; 95% CI, 1.01 to 6.6) and birthplace in Asia (adjusted hazard ratio, 3.4; 95% CI, 1.4 to 8.3), whereas rifampin-associated adverse events were associated with age over 60 years (adjusted hazard ratio, 3.9; 95% CI, 1.02 to 14.9) and human immunodeficiency virus-positive status (adjusted hazard ratio, 8.0; 95% CI, 1.5 to 43). The incidence of pyrazinamide-induced hepatotoxicity and rash during treatment for active TB was substantially higher than with the other first-line anti-TB drugs, and higher than previously recognized.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".