Adverse reactions to first-line antituberculosis drugs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it