Bibliographic record
Abstract
INTRODUCTION: Tuberculosis continues to be a major cause of morbidity and mortality worldwide. Currently available drugs are effective for treatment of the disease or latent infection, but may cause serious adverse effects. METHODS: The authors reviewed the literature for side effects of five first-line antituberculous medications (isoniazid, rifampin, pyrazinamide, ethambutol and streptomycin). Incidence of the major side effects were compiled with particular attention to the incidence of isoniazid hepatotoxicity. RESULTS: Hepatotoxicity to isoniazid is a serious problem. Although overall incidence may be decreasing, incidence averaged 9.2 per 1000 patients who were compliant, in multiple studies, with a case fatality rate of 4.7%. The incidence is higher with increasing age. Other serious adverse effects include dermatological, gastrointestinal, hypersensitivity, neurological, haematological and renal reactions. They can lead to drug discontinuation (in up to 10% of patients) or even more serious morbidity or mortality. CONCLUSIONS: Side effects to antituberculosis drugs are common, and include hepatitis, cutaneous reactions, gastrointestinal intolerance, haematological reactions and renal failure. These adverse effects must be recognised early, to reduce associated morbidity and mortality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".