Predicting outcomes and drug resistance with standardised treatment of active tuberculosis
Bibliographic record
Abstract
New World Health Organization guidelines recommend initial treatment of active tuberculosis (TB) with a 6-month regimen utilising rifampin throughout. We have modelled expected treatment outcomes, including drug resistance, with this regimen, compared to an 8-month regimen with rifampin for the first 2 months only, followed by standardised retreatment. A deterministic model was used to predict treatment outcomes in hypothetical cohorts of 1,000 new smear-positive cases from seven countries with varying prevalence of initial drug resistance. Model inputs were taken from published systematic reviews. Predicted outcomes included number of deaths, failures and relapses, plus the proportion with drug resistance. Sensitivity analyses examined different risks of acquired drug resistance. Compared to use of the standardised 8-month regimen, for every 1,000 new TB cases treated with the 6-month regimen we predict that 48-86 fewer persons will require retreatment, and 3-12 deaths would be avoided. However, the proportion failing or relapsing after retreatment is predicted to be higher, because with the 6-month regimen 50-94% of failures and 3-56% of relapses will have multidrug-resistant TB. We predict substantial public health benefits from changing from the 8-month to the 6-month regimen. However in almost all settings the current standardised retreatment regimen will no longer be adequate.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".