Systematic screening for tuberculosis among hospital outpatients in Cameroon: The role of screening and testing algorithms to improve case detection
Bibliographic record
Abstract
BACKGROUND: Better screening and testing approaches are needed to improve TB case finding, particularly in health facilities where many people with TB seek care but are not diagnosed using the existing approaches. OBJECTIVE: We aimed to evaluate the performance of various TB screening and testing approaches among hospital outpatients in a setting with a high prevalence of HIV/TB. METHODS: We screened outpatients at a large hospital in Cameroon using both chest X-ray and a symptom questionnaire including current cough, fever, night sweats and/or weight loss. Participants with a positive screen were tested for TB using smear microscopy, the Xpert MTB/RIF assay, and culture. RESULTS: Among 2051 people screened, 1137 (55%) reported one or more TB symptom and 389 (19%) had an abnormal chest X-ray. In total, 1255 people (61%) had a positive screen and 31 of those screened (1.5%) had bacteriologically confirmed TB. To detect TB, screening with cough >2 weeks had a sensitivity of 61% (95% CI, 44-78%). Screening for a combination of cough >2 -weeks and/or abnormal chest X-ray had a sensitivity of 81% (95% CI, 67-95%) and specificity of 71% (95% CI, 69-73%), while screening for a combination of cough >2 weeks or any of 2 or more symptoms had a similar performance. Smear microscopy and Xpert MTB/RIF detected 32% (10/31) and 55% (17/31), respectively, of people who had bacteriologically-confirmed TB. CONCLUSIONS: Screening hospital outpatients for cough >2 weeks or for at least 2 of current cough, fever, night sweats or weight loss is a feasible strategy that had a high relative yield to detect bacteriologically-confirmed TB in this population. Clinical diagnosis of TB is still an important need, even where Xpert MTB/RIF testing is available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".