In closely monitored patients, adherence in the first month predicts completion of therapy for latent tuberculosis infection.
Bibliographic record
Abstract
BACKGROUND: Current therapy for latent TB infection (LTBI) is long, and requires close follow-up. This results in sub-optimal adherence-the major reason for failure of therapy. METHODS: In an open label randomised trial comparing 4 months of rifampicin with 9 months of isoniazid, the proportion and regularity of doses taken, measured with an electronic monitoring system (MEMS), and provider estimates of adherence in the first month of therapy, were assessed as predictors of treatment completion. RESULTS: Of 104 patients analysed, 86 took more than 80% of doses within the expected interval, 11 took more than 80% of doses but over a longer time interval than usually allowed, and seven did not complete treatment. Treatment completion was associated with the number of doses taken, and the variability of intervals between doses during the first month of treatment. CONCLUSIONS: Adherence in the first month, based on the number of doses and variability of times when taken, could be useful to predict completion of LTBI therapy. Interventions could be targeted to patients with suboptimal adherence in the first month.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".