Harnessing new mHealth technologies to Strengthen the Management of Multidrug-Resistant Tuberculosis in Vietnam (V-SMART trial): a protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Multidrug-resistant tuberculosis (MDR-TB) remains a major public health problem globally. Long, complex treatment regimens coupled with frequent adverse events have resulted in poor treatment adherence and patient outcomes. Smartphone-based mobile health (mHealth) technologies offer national TB programmes an appealing platform to improve patient care and management; however, clinical trial evidence to support their use is lacking. This trial will test the hypothesis that an mHealth intervention can improve treatment success among patients with MDR-TB and is cost-effective compared with standard practice. METHODS AND ANALYSIS: A community-based, open-label, parallel-group randomised controlled trial will be conducted among patients treated for MDR-TB in seven provinces of Vietnam. Patients commencing therapy for microbiologically confirmed rifampicin-resistant or multidrug-resistant tuberculosis within the past 30 days will be recruited to the study. Participants will be individually randomised to an intervention arm, comprising use of an mHealth application for treatment support, or a 'standard care' arm. In both arms, patients will be managed by the national TB programme according to current national treatment guidelines. The primary outcome measure of effectiveness will be the proportion of patients with treatment success (defined as treatment completion and/or bacteriological cure) after 24 months. A marginal Poisson regression model estimated via a generalised estimating equation will be used to test the effect of the intervention on treatment success. A prospective microcosting of the intervention and within-trial cost-effectiveness analysis will also be undertaken from a societal perspective. Cost-effectiveness will be presented as an incremental cost per patient successfully treated and an incremental cost per quality-adjusted life-year gained. ETHICS: Ethical approval for the study was granted by The University of Sydney Human Research Ethics Committee (2019/676). DISSEMINATION: Study findings will be disseminated to participants and published in peer-reviewed journals and conference proceedings. TRIAL REGISTRATION NUMBER: ACTRN12620000681954.
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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 | 0.059 | 0.047 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".