Effect of implementation of a 12-dose once-weekly treatment (3HP) in addition to standard regimens to prevent TB on completion rates: Interrupted time series design
Bibliographic record
Abstract
OBJECTIVES: We aimed to determine if offering a 12-dose once-weekly treatment (3HP) as an additional treatment option would result in an increase in the overall proportion of patients completing TB preventive treatment (TPT) above the baseline rate. METHODS: We analyzed outcomes in consecutive adults referred to a TB clinic from January 2010 to May 2019. Starting December 2016, 3HP was offered as an alternative to standard clinic regimens which included 9 months of daily isoniazid or 4 months of daily rifampin. The primary outcome was the proportion of patients who completed TPT among all patients who started treatment. Using segmented autoregression analysis, we compared completion at the end of the study with projected completion had the intervention not been introduced. RESULTS: A total of 2803 adults were referred for assessment over the study period. There was an absolute increase in completions among those who started a treatment of 19.0% at the end of the study between the observed intervention completion rate and the projected completion rate from the baseline study period (the completion rate had the 3HP intervention not been introduced) (76% observed vs 57% projected; 95% CI 6.6 to 31.4%; p = 0.004) and an absolute increase among those who were offered treatment (17.3%; 95% CI, 2.3 to 32.3%; p = 0.025). CONCLUSIONS: The introduction of 3HP for TPT as an alternative to the regular regimens offered resulted in a significant increase in the proportion of patients completing treatment. Our study provides evidence to support accelerated use of 3HP in Canada.
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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 | 0.040 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".