Pooling of sputum samples to increase tuberculosis diagnostic capacity in Brazil during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: We assessed whether combining (pooling) four individual's samples and testing with Xpert Ultra has the same accuracy as testing samples individually as a more efficient testing method. METHODS: We conducted a cross-sectional study of individuals with presumptive tuberculosis attending primary health care or general hospital facilities in Alagoas, Brazil. The sputum samples of four consecutive individuals were pooled and the pool and individual samples were tested with Xpert Ultra. The agreement of the tests was compared using kappa statistics. We estimated the sensitivity and specificity of pooling using the individual test as the reference standard and potential cartridge savings. RESULTS: A total of 396 participants were tested. A total of 95 (24.0%) individual samples were Mycobacterium tuberculosis (MTB)-positive, 300 (75.8%) "MTB not detected", including 20 "MTB trace", and one reported an error. A total of 99 pools of four samples were tested, of which 62 (62.6%) had MTB detected and 37 (37.4%) MTB not detected, including six (6.1%) with MTB trace. The agreement between individual and pooled testing was 96.0%. Pooling had a sensitivity of 95.0% (95% confidence interval 86.9-99%), specificity of 97.1% (95% confidence interval 85.1-99.9%), and kappa of 0.913. The method saved 12.4% of cartridge costs. CONCLUSION: The pooled testing of specimens had a high level of agreement with individual testing. The pooling of samples for testing improves the efficiency of testing, potentially enabling the screening and testing of larger numbers of individuals more cost-effectively.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".