Increasing Detection and Confirmation of Tuberculosis in Children in Southern Ethiopia: Pooled Samples Tested Using Microscopy and Xpert
Bibliographic record
Abstract
Background: Childhood tuberculosis accounts for about 10% of estimated TB cases in the world. Despite advances in diagnostics, childhood TB remains a challenge. We evaluated pooling method and testing with GeneXpert MTB/RIF in southern Ethiopia. Methods: This is a cross-sectional study in presumptive TB children st, 2nd and pooled samples. Results: Of 340 presumptive TB cases enrolled, 96 and 244 children submitted gastric aspirate and sputum samples respectively. Of 1020 samples collected (282 gastric aspirate and 738 sputum samples), 38 (3.7%) were positive by Xpert (10 (3.5%) from gastric aspirate and 28 (3.8%) from sputum sample). Similarly, 8 (1.2%) of sputum samples were positive by ZN but none from gastric aspirate. Of 244 children who submitted sputum samples, 3 (1.2%) were bacteriologically positive compared to 12 (4.9%) by Xpert. Of 96 children who submitted gastric aspirate samples, none were positive by ZN while 5 (5.2%) were positive by Xpert. Of bacteriologically confirmed TB cases 0.9% was by ZN and 4.7% by Xpert, an increase of 3.8%. Pooled testing increased positivity by 0.3% for ZN and 1.5% by Xpert compared to the 1st sample. Conclusions: Xpert MTB/RIF testing increases yield compared to ZN testing for gastric aspirate samples. The same-day approach and pooling samples improves efficient use of cartridge, reduce the number of visits for seeking diagnosis and save resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".