Chest radiography for active tuberculosis case finding in the homeless: a systematic review and meta-analysis
Bibliographic record
Abstract
SETTING: In low-incidence regions, tuberculosis (TB) often affects vulnerable populations. Guidelines recommend active case finding (ACF) in homeless populations, but there is no consensus on a preferred screening method. OBJECTIVE: We performed a systematic review and meta-analysis to evaluate the use of chest X-ray (CXR) screening in ACF for TB in homeless populations. DESIGN: Articles were identified through EMBASE, Medline and the Cochrane Library. Studies using symptom screens, CXRs, sputum sweeps, tuberculin skin tests and/or interferon-gamma release assays to detect active TB in homeless populations were sought. Data were extracted using a standardised method by two reviewers and validated with an objective tool. RESULTS: Sixteen studies addressing CXR screening of homeless populations for active TB in low-incidence regions were analysed. The pooled prevalence of active TB in the 16 study cohorts was 931 per 100 000 population screened (95%CI 565-1534) and 782/100 000 CXR performed (95%CI 566-1079). Six of seven longitudinal screening programs reported a reduction in regional TB incidence after implementation of the CXR-based ACF programme. CONCLUSION: Our data suggest that CXR screening is a good tool for ACF in homeless populations in low-incidence regions.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".