Enhancing tuberculosis (TB) case detection among hospitalized patients through lay health worker led screening: a before-and-after study in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
Tuberculosis (TB) among hospitalized patients is underdiagnosed. This study assessed systematic TB-screening, followed by an enhanced TB-diagnostic package for hospitalized patients implemented by trained lay health workers in KwaZulu-Natal, South Africa. In this before-and-after study we included patients ≥ 18 years. The intervention consisted of systematic clinical screening for TB, HIV and diabetes mellitus by lay health workers and provision of an enhanced TB-diagnostic package including sputum Xpert MTB/Rif Ultra, urine lateral-flow lipoarabinomannan assay (LF-LAM), chest x-ray, and sputum culture. We compared TB case findings with people hospitalized one year preceding the intervention. In the pre-intervention phase, 5217 people were hospitalized. Among 4913 (94.2%) people not on TB treatment, 367 (7.5%) were diagnosed with TB. In the intervention phase, 4015 eligible people were hospitalized. Among 3734 (93.0%) people not on TB treatment, 560 (15.0%) were diagnosed with TB. The proportion of patients diagnosed with TB was higher in the intervention phase (15.0% vs. 7.5%, p < 0.001). Overall in-hospital mortality was lower in the intervention phase [166/3734(4.5%) vs. 336/4913(6.8%), p < 0.001]. Lay health worker-led implementation of systematic TB-screening, coupled with provision of an enhanced TB-diagnostic package significantly improved TB case detection and mortality among hospitalized adults.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".