A public health approach to increase treatment of latent TB among household contacts in Brazil
Bibliographic record
Abstract
SETTING: Two consecutive trials were conducted to evaluate the effectiveness of a public health approach to identify and correct problems in the care cascade for household contacts (HHCs) of TB patients in three Brazilian high TB incidence cities.METHODS: In the first trial, 12 clinics underwent standardised evaluation using questionnaires administered to TB patients, HHCs and healthcare workers, and analysis of the cascade of latent TB care among HHCs. Six clinics were then randomised to receive interventions to strengthen management of latent TB infection (LTBI), including in-service training provided by nurses, work process organisation and additional clinic-specific solutions. In the second trial, a similar but streamlined evaluation was conducted in two clinics, who then received initial and subsequent intensive in-service training provided by a physician.RESULTS: In the evaluation phase of both trials, many HHCs were identified, but few started LTBI treatment. After the intervention, the number of HHCs initiating treatment per 100 active TB patients increased by 10 (95%CI ‾11 to 30) in the first trial, and by 44 (95%CI 26 to 61) in the second trial.DISCUSSION: A public health approach with standardised evaluation, local decisions for improvements, followed by intensive initial and in-service training appears promising for improved LTBI management.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".