What Is the Optimal Community-Based Tuberculosis Screening Algorithm for People Who Inject Drugs in a High-Burden Setting?
Bibliographic record
Abstract
Background: Although people who inject drugs (PWID) are a high-risk group for tuberculosis (TB), current case-finding strategies fail to identify most TB cases. There is a need for an optimized community-based algorithm to improve TB detection in such disproportionately affected populations. Methods: Using respondent-driven sampling, we recruited PWID at community sites in Hai Phong, Vietnam, screening for classic TB symptoms, C-reactive protein blood measurement, portable on-site chest x-ray with CAD4TB software (Computer-Aided Detection for Tuberculosis version 7; Delft Imaging Systems BV), and Xpert MTB/RIF on sputum. Any participants suspected of TB by on-site physicians were referred to the infectious disease hospital for confirmatory testing, and external experts validated final diagnoses, which were then used as the TB gold standard. We aimed to identify the screening algorithm with the highest case detection at the lowest cost among different on-site testing combinations. Ingredients-based costing was used to evaluate the cost per test and cost per case detected for each algorithm. Results: Among the 1080 PWID enrolled, 47 (4.4%; 95% CI, 2.8%-6.4%) were diagnosed with TB disease. When compared with the current symptom-based TB screening strategy in Vietnam (double D), systematic chest x-ray with CAD4TB, Xpert MTB/RIF for those with CAD4TB ≥50, and referral to care for those with either CAD4TB ≥70 or a positive Xpert test result doubled the sensitivity (82.9% vs 43.9%) and yield (3.2% vs 1.7%) while maintaining a reasonable cost per TB case detected (US $439 vs $309 for standard of care). Conclusions: We defined an acceptable and moderate cost algorithm that improves efficiency for community-based TB screening among PWID in Vietnam. To reflect real TB prevalence, we make the case that active case finding and systematic screening strategies should not limit testing to those with a positive symptom screen.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".