Evaluation of OMNIgene<SUP>®</SUP>•SPUTUM-stabilised sputum for long-term transport and Xpert<SUP>®</SUP> MTB/RIF testing in Nepal
Bibliographic record
Abstract
SETTING: German Nepal TB Project, National Tuberculosis Reference Laboratory, Kathmandu, Nepal. OBJECTIVE: To evaluate whether transporting samples in OMNIgene®•SPUTUM (OM-S) reagent from a peripheral collection site to a central laboratory in Nepal can improve tuberculosis (TB) detection and increase the sensitivity of Xpert® MTB/RIF testing. DESIGN: One hundred sputum samples were split manually. Each portion was assigned to the OM-S group (OM-S added at collection, airline-couriered without cold chain, no other processing required) or the standard-of-care (SOC) group (samples airline-couriered on ice, sodium hydroxide + N-acetyl-L-cysteine processing required at the laboratory). Smear microscopy and Xpert testing were performed. RESULTS: Transport time was 2-13 days. Overall smear results were comparable (respectively 58% and 56% smear-negative results in the OM-S and SOC groups). The rate of smear-positive, Mycobacterium tuberculosis-positive (MTB+) sample detection was identical for both treatment groups, at 95%. More smear-negative MTB+ samples were detected in the OM-S group (17% vs. 13%, P = 0.0655). CONCLUSION: Sputum samples treated with OM-S can undergo multiday ambient-temperature transport and yield comparable smear and Xpert results to those of SOC samples. Further investigation with larger sample sizes is required to assess whether treating sputum samples with OM-S could increase the sensitivity of Xpert testing in smear-negative samples.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".