Needs assessment and preparedness of the primary health care network for scaling-up preventive tuberculosis treatment in 5 Brazilian capitals
Bibliographic record
Abstract
This study aims to conduct a needs and preparedness assessment of public primary care units to scale up tuberculosis infection diagnosis and tuberculosis preventive treatment in 5 Brazilian capitals. This observational operational study was carried out across five Brazilian high tuberculosis-burden cities. Clinics with at least one monthly new tuberculosis case were included. Data on Purified Protein Derivative (PPD) storage, tuberculin skin testing (TST) and interferon-gamma release assay (IGRA) availability, personnel qualified for performing TST, radiological facilities and tuberculosis preventive treatment drug availability, were gathered between August 2023 and January 2024. Out of 285 clinics included, 78% (CI95%: 73%-82%) did not offer TST on-site, with only 28% (CI95%: 22%3%) having staff qualified to perform TST, and 35% (CI95%: 29%-40%) lacking dedicated refrigerators for PPD storage. Most (97%, CI95%: 94%-99%) clinics did not collect IGRA testing, with an average distance of 6.7 km (CI95%: 5%-7%) to IGRA labs and a turnaround time of 11.7 days (CI95%: 9%13%) for results. Most (87%, CI95%: 83%-91%) do not offer on-site radiological testing. The primary care network was unprepared to scaling up tuberculosis infection testing. Key issues include unavailability of TST mainly because of insufficient qualified personnel. Without accelerated qualification of staff for TST, scaling up tuberculosis preventive treatment will be impossible.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".