Protocol for a pragmatic randomised controlled trial to evaluate the effectiveness of improving tuberculosis patients' treatment adherence via electronic monitors and an app versus usual care in Tibet
Bibliographic record
Abstract
Abstract Background: Treatment non-adherence is a serious challenge to effective tuberculosis (TB) control in Tibet. In this study, we will pilot and evaluate the effectiveness of using new technologies, including electronic monitors (e-monitors) and a smartphone app, to improve treatment adherence among new pulmonary TB patients in Tibet, China. Methods: This is a prospective, pragmatic, multicentre, individual-randomized trial with blinded evaluation of outcomes and data analysis, and unblinded treatment. New pulmonary TB patients of Shigatse, Tibet will be randomized to either the intervention or control arm in a 1:1 ration at the time of their diagnoses. All patients will be treated according to the WHO standard TB treatment regimens and China National TB program guidelines. In the control arm, patients will receive their medicines in e-monitors that are deactivated for its reminder function but are able to collect medication adherence history. In the intervention arm, patients will take medications in e-monitors that will record their medication adherence and share with health staff via a smartphone app. Patients will be opted to receive video observed treatment when adherence is problematic. The primary outcome is the rate of poor adherence measured per month during their treatment. It will be calculated from monthly-level data for each patient indicating the number of doses missed per month, with poor monthly adherence defined as the patient having missed ≥20% of doses per month. We will conduct a qualitative process evaluation to explore operational questions regarding acceptability, cultural appropriateness and burden of technology use, a cost-effectiveness analysis and long-term effects on TB control. Discussion: Our study is one of the first trials to evaluate the use of e-monitors and smartphone app to improve communications between TB patients and health staff in low-and-middle income countries (LMICs). All intervention activities are designed to be embedded into routine practice of TB care with strong local ownership. Through the trial, we intend to understand the effectiveness of our intervention, as well as its feasibility, cost-effectiveness and long-term impact to inform future scale-up in remote areas of China and LMICs. Trial Registration: Current Controlled Trials ISRCTN52132803, registered on 9 November 2018. Keywords: Tuberculosis, treatment adherence, mhealth, randomised controlled trial, Tibet
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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 | 0.035 | 0.034 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.010 |
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".