Pooled Nucleic Acid Amplification Test for Screening of Stool Specimens for Shiga Toxin-Producing Escherichia coli
Bibliographic record
Abstract
ABSTRACT Shiga toxin-producing Escherichia coli (STEC)-associated enteric illness is attributed to O157 and non-O157 serotypes; however, traditional culture-based methods underdetect non-O157 STEC. Labor and cost of consumables are major barriers to implementation of the CDC recommendation to test all stools for both O157 and non-O157 serotypes. We evaluated the feasibility of a pooled nucleic acid amplification test (NAAT) as an approach for screening stool specimens for STEC. For retrospective evaluation, 300 stool specimens were used to create pools of 10 samples each. The sensitivity was 83% for the preenrichment pooling strategy and 100% for the postenrichment pooling strategy compared with those for individual NAAT results. The difference in cycle threshold ( C T ) between individual and pooled NAAT results for specimens was significantly lower and more consistent for postenrichment pooling ( stx 1 mean = 3.90, stx 2 mean = 4.28) than those for preenrichment pooling (excluding undetected specimens; stx 1 mean = 9.34, stx 2 mean = 8.96) ( P ≤ 0.0013). Cost of consumables and labor time savings of 48 to 81% and 6 to 66%, respectively, were estimated for the testing of 90 specimens by the postenrichment pooled NAAT strategy on the basis of an expected 1 to 2% positivity rate. A 30-day prospective head-to-head clinical trial involving 512 specimens confirmed the sensitivity and labor savings associated with the postenrichment pooled NAAT strategy. The postenrichment pooled NAAT strategy described here is suitable for efficient large-scale surveillance of all STEC serotypes. Comprehensive detection of STEC will result in accurate estimation of STEC burden and, consequently, appropriate public health interventions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".