TB case fatality and recurrence in a private sector cohort in Mumbai, India
Bibliographic record
Abstract
BACKGROUND: Half of India´s three million TB patients are treated in the largely unregulated private sector, where quality of care is often poor. Private provider interface agencies (PPIAs) seek to improve private sector quality of care, which can be measured in terms of case fatality and recurrence rates.METHODS: We conducted a retrospective cohort survey of 4,000 private sector patients managed by the PATH PPIA between 2014 and 2017. We estimated treatment and post-treatment case-fatality ratios (CFRs) and recurrence rates. We used Cox proportional hazards models to identify predictors of fatality and recurrence. Patient loss to follow-up was adjusted for using selection weighting.RESULTS: The treatment CFR was 7.1% (95% CI 6.0–8.2). At 24 months post-treatment, the CFR was 2.4% (95% CI 1.7–3.0) and the recurrence rate was 1.9% (95% CI 1.3–2.5). Treatment fatality was associated with age (HR 1.02, 95% CI 1.02–1.03), clinical diagnosis (HR 0.61, 95% CI 0.45–0.84), treatment duration (HR 0.09, 95% CI 0.06–0.10) and adherence. Post-treatment fatality was associated with treatment duration (HR 0.87, 95% CI 0.79–0.91) and adherence.CONCLUSIONS: We found a moderate treatment phase CFR among PPIA-managed private sector patient with low rates of post-treatment fatality and recurrence. Routine monitoring of patient outcomes after treatment would strengthen PPIAs and inform future post TB interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".