Effectiveness of a comprehensive package based on electronic medication monitors at improving treatment outcomes among tuberculosis patients in Tibet: a multi-centre randomised controlled trial
Bibliographic record
Abstract
Electronic medication monitors (EMMs) are recommended to complement directly observed treatment (DOT) for tuberculosis (TB) but without conclusive evidence. We conducted this pragmatic, superiority trial in six counties in Shigatse, Tibet. Eligible participants were drug-susceptible TB patients aged ≥15 years starting standard TB treatment. Intervention patients received an EMM box. This included audio medication-adherence reminders and recorded box-opening data, which were transmitted to a cloud-based server accessible to healthcare providers to allow remote adherence monitoring. A linked smartphone app enabled communication between patients and healthcare providers. Control patients received usual care plus a deactivated EMM. Our primary outcome was poor monthly adherence and other secondary treatment outcomes based on national tuberculosis reporting data. We recruited 143 patients to the intervention and 135 to the control. In the intervention arm 10.2% of patient treatment months showed poor adherence compared to 36.5% in the control arm. The corresponding intervention versus control adjusted risk difference was -29.2 percentage points (95% CI: -35.3, -22.2; p≤0.001). Five out of six secondary treatment outcomes also demonstrated clear improvements including treatment success, which was 93.7% in the intervention arm and 73.1% in the control arm, with an adjusted risk difference of 21 percentage points (95% CI: 12.4, 29.4); p≤0.001. Our interventions were considerably effective at improving TB treatment adherence and outcomes, suggesting the comprehensive package for LMICs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".