Comparing cost-effectiveness of standardised tuberculosis treatments given varying drug resistance
Bibliographic record
Abstract
There is a growing need to identify appropriate standardised treatment strategies that will adequately treat various forms of drug-resistant tuberculosis (TB) and prevent multidrug-resistant (MDR)-TB. A Markov model estimated treatment-related acquired MDR-TB, mortality, disability-adjusted life years and costs in settings with different prevalence of isoniazid monoresistant TB and MDR-TB. We compared four treatment strategies: 1) the standard World Health Organization recommended treatment strategy; 2) adding ethambutol throughout the 6-month treatment of new cases; 3) using a strengthened standardised retreatment regimen; and 4) using standardised MDR treatment for failures of initial treatment. Treatment-related outcomes were derived from the published literature, and costs from direct surveys. A strengthened retreatment regimen, which could achieve lower failure, relapse and acquired MDR rates in isoniazid monoresistant cases, was predicted to be the most cost-effective strategy in all modelled settings. Empirical MDR treatment of failures of initial treatment was the most costly strategy but resulted in the fewest deaths. Adding ethambutol throughout initial treatment would be most effective in preventing acquired MDR, but would lead to excess cases of blindness. A high priority should be given to improving the standardised retreatment regimen, as this is predicted to produce greater benefits than other recently recommended strategies.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".