Low Body Mass Index at Treatment Initiation and Rifampicin-Resistant Tuberculosis Treatment Outcomes: An Individual Participant Data Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The impact of low body mass index (BMI) at initiation of rifampicin-resistant tuberculosis (RR-TB) treatment on outcomes is uncertain. We evaluated the association between BMI at RR-TB treatment initiation and end-of-treatment outcomes. METHODS: We performed an individual participant data meta-analysis of adults aged ≥18 years with RR-TB whose BMI was documented at treatment initiation. We compared odds of any unfavorable treatment outcome, mortality, or failure/recurrence between patients who were underweight (BMI <18.5 kg/m2) and not underweight. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using logistic regression, with matching on demographic, clinical, and treatment-related factors. We evaluated effect modification by human immunodeficiency virus (HIV) status and other variables using likelihood ratio tests. We also estimated cumulative incidence of mortality during treatment stratified by HIV. RESULTS: Overall, 5148 patients were included; 1702 (33%) were underweight at treatment initiation. The median (interquartile range) age was 37 years (29 to 47), and 455 (9%) had HIV. Compared with nonunderweight patients, the aOR among underweight patients was 1.7 (95% CI, 1.4-1.9) for any unfavorable outcome, 3.1 (2.4-3.9) for death, and 1.6 (1.2-2.0) for failure/recurrence. Significant effect modification was found for World Health Organization region of treatment. Among HIV-negative patients, 24-month mortality was 14.8% (95% CI, 12.7%-17.3%) for underweight and 5.6% (4.5%-7.0%) for not underweight patients. Among patients with HIV, corresponding values were 33.0% (25.6%-42.6%) and 20.9% (14.1%-27.6%). CONCLUSIONS: Low BMI at treatment initiation for RR-TB is associated with increased odds of unfavorable treatment outcome, particularly mortality.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".