The impact and cost-effectiveness of strategies to detect drug-resistant tuberculosis
Bibliographic record
Abstract
Drug-resistant tuberculosis (TB) is a serious emerging problem in many low-resource countries. TB control programmes are uncertain of which drug susceptibility tests (DSTs) to use and when to test patients. We predicted the potential cost-effectiveness of different DST strategies, in settings with varying prevalence of drug resistance. Using decision analysis, we assessed the cost-effectiveness of conventional and rapid DSTs for previously diagnosed smear-positive TB cases. Five different time-points were considered for administering DSTs. Different initial drug resistance and HIV scenarios were also considered. All DST scenarios in the wide range of settings considered were found to be cost-effective. The strategy of performing a rapid DST that detects any form of isoniazid (INH) and rifampicin (RIF) resistance for all patients before the initiation of treatment was predicted to be the most cost-effective strategy. In a setting with moderate drug resistance, the cost per disability-adjusted life year gained was as low as US$744. Our findings support the roll-out of rapid drug susceptibility testing at the moment of diagnosis to detect any form of INH and RIF resistance in all countries with moderate or greater burdens of drug-resistant TB.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".