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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".