The effect of weekly text-message communication on treatment completion among patients with latent tuberculosis infection: study protocol for a randomised controlled trial (WelTel LTBI)
Bibliographic record
Abstract
INTRODUCTION: Interventions to improve adherence to treatment for latent tuberculosis infection (LTBI) are necessary to improve treatment completion rates and optimise tuberculosis (TB) control efforts. The high prevalence of cell phone use presents opportunities to develop innovative ways to engage patients in care. A randomised controlled trial (RCT), WelTel Kenya1, demonstrated that weekly text messages improved antiretroviral adherence and clinical outcomes among patients initiating HIV treatment. The aim of this study is to determine whether the WelTel intervention can improve treatment completion among patients with LTBI and to evaluate the intervention's cost-effectiveness. METHODS AND ANALYSIS: This open, two-site, parallel RCT (WelTel LTBI) will be conducted at TB clinics in Vancouver and New Westminster, British Columbia, Canada. Over 2 years, we aim to recruit 350 individuals initiating a 9-month isoniazid regimen. Participants will be randomly allocated to an intervention or control (standard care) arm in a 1:1 ratio. Intervention arm participants will receive a weekly text-message 'check-in' to which they will be asked to respond within 48 h. A TB clinician will follow-up instances of non-response and problems that are identified. Participants will be followed until treatment completion (up to 12 months) or discontinuation. The primary outcome is self-reported treatment completion (taking ≥80% of doses within 12 months). Secondary outcomes include daily adherence (percentage of days participants used medication as prescribed) and time to treatment completion. Patient satisfaction with the intervention will be evaluated, and the intervention's cost-effectiveness will be analysed through decision-analytic modelling. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the University of British Columbia. This trial will test the efficacy and cost-effectiveness of the WelTel intervention to improve treatment completion among patients with LTBI. Trial results and economic evaluation will help inform policy and practice on the use of WelTel in this population. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT01549457.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 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".