Validation of a Modified ANSR® for E. coli O157:H7 Method for Detection of E. coli O157:H7 in Select Foods
Bibliographic record
Abstract
Abstract Background: The ANSR method is based on isothermal nucleic acid amplification technology. The modifications to assay components improve sensitivity of the assay and robustness of the internal positive control. Objective: A Performance Tested MethodSM validation study was conducted to assess performance of a modified version of the ANSR® for Escherichia coli O157:H7 method. Methods: The validation study included inclusivity/exclusivity, matrix, robustness, accelerated stability, and independent laboratory testing. Results: In inclusivity testing of 55 strains of E. coli O157:H7 and E. coli O157:NM variants, all strains produced positive results. In exclusivity testing of 41 strains including E. coli of other serotypes and bacteria of closely related genera, all strains produced negative results. In matrix testing of beef trim, raw ground beef, spinach, and sprout-irrigation water, ANSR method performance was compared with that of the U.S. Department of Agriculture Food Safety and Inspection Service Microbiology Laboratory Guidebook or the U.S. Food and Drug Administration Bacteriological Analytical Manual reference culture procedures. Conclusions: all trials, ANSR method performance was not statistically different from that of the reference methods. Results of independent laboratory testing of ground beef corroborated those of internal testing. Introducing modest changes to three assay operating parameters did not materially affect ANSR method performance. Finally, accelerated stability testing results of three independently manufactured lots of ANSR reagents support a shelf-life of 1 year when stored at 2–8°C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".