Analysis of loss to follow-up in 4099 multidrug-resistant pulmonary tuberculosis patients
Bibliographic record
Abstract
Loss to follow-up (LFU) of ≥2 consecutive months contributes to the poor levels of treatment success in multidrug-resistant tuberculosis (MDR-TB) reported by TB programmes. We explored the timing of when LFU occurs by month of MDR-TB treatment and identified patient-level risk factors associated with LFU.We analysed a dataset of individual MDR-TB patient data (4099 patients from 22 countries). We used Kaplan-Meier survival curves to plot time to LFU and a Cox proportional hazards model to explore the association of potential risk factors with LFU.Around one-sixth (n=702) of patients were recorded as LFU. Median (interquartile range) time to LFU was 7 (3-11) months. The majority of LFU occurred in the initial phase of treatment (75% in the first 11 months). Major risk factors associated with LFU were: age 36-50 years (HR 1.3, 95% CI 1.0-1.6; p=0.04) compared with age 0-25 years, being HIV positive (HR 1.8, 95% CI 1.2-2.7; p<0.01) compared with HIV negative, on an individualised treatment regimen (HR 0.7, 95% CI 0.6-1.0; p=0.03) compared with a standardised regimen and a recorded serious adverse event (HR 0.5, 95% CI 0.4-0.6; p<0.01) compared with no serious adverse event.Both patient- and regimen-related factors were associated with LFU, which may guide interventions to improve treatment adherence, particularly in the first 11 months.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".