Latent tuberculous infection in the United States and Canada: who completes treatment and why?
Bibliographic record
Abstract
OBJECTIVES: To assess latent tuberculous infection (LTBI) treatment completion rates in a large prospective US/Canada multisite cohort and identify associated risk factors. METHODS: This prospective cohort study assessed factors associated with LTBI treatment completion through interviews with persons who initiated treatment at 12 sites. Interviews were conducted at treatment initiation and completion/cessation. Participants received usual care according to each clinic's procedure. Multivariable models were constructed based on stepwise assessment of potential predictors and interactions. RESULTS: Of 1515 participants initiating LTBI treatment, 1323 had information available on treatment completion; 617 (46.6%) completed treatment. Baseline predictors of completion included male sex, foreign birth, not thinking it would be a problem to take anti-tuberculosis medication, and having health insurance. Participants in stable housing who received monthly appointment reminders were more likely to complete treatment than those without stable housing or without monthly reminders. End-of-treatment predictors of non-completion included severe symptoms and the inconvenience of clinic/pharmacy schedules, barriers to care and changes of residence. Common reasons for treatment non-completion were patient concerns about tolerability/toxicity, appointment conflicts, low prioritization of TB, and forgetfulness. CONCLUSIONS: Less than half of treatment initiators completed treatment in our multisite study. Addressing tangible issues such as not having health insurance, toxicity concerns, and clinic accessibility could help to improve treatment completion rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".