The effect of text messaging on latent tuberculosis treatment adherence: a randomised controlled trial
Bibliographic record
Abstract
There is limited high-quality evidence available to inform the use of text messaging to improve latent tuberculosis infection (LTBI) treatment adherence.We performed a parallel, randomised controlled trial at two sites to assess the effect of a two-way short message service on LTBI adherence. We enrolled adults initiating LTBI therapy from June 2012 to September 2015 in British Columbia, Canada. Participants were randomised in a 1:1 ratio to standard LTBI treatment (control) or standard LTBI treatment plus two-way weekly text messaging (intervention). The primary outcome was treatment completion, defined as taking ≥80% prescribed doses within 12 months (isoniazid) or 6 months (rifampin) of enrolment. The trial was unblinded except for the data analyst.A total of 358 participants were assigned to the intervention (n=170) and control (n=188) arms. In intention-to-treat analysis, the proportion of participants completing LTBI therapy in the intervention and control arms was 79.4% and 81.9%, respectively (RR 0.97, 95% CI 0.88-1.07; p=0.550). Results were similar for pre-specified secondary end-points, including time-to-completion of LTBI therapy, completion of >90% of prescribed LTBI doses and health-related quality of life.Weekly two-way text messaging did not improve LTBI completion rates compared to standard LTBI care; however, completion rates were high in both treatment arms.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".