Performance of four commercial real-time PCR assays for the detection of bacterial enteric pathogens in clinical samples
Bibliographic record
Abstract
Objectives Many laboratories use culture-independent diagnostic tests for bacterial gastroenteritis (i.e. real-time polymerase chain reaction, RT-PCR) instead of culture because of better sensitivity, automation, and faster turnaround times. To address some gaps in initial evaluations and lack of intraassay comparisons for many commercial RT-PCRs, this study compared the ability of four commercially available RT-PCR tests (Ridagene, Fast Track Diagnostics, BD Max, and Prodesse Progastro) to detect five major bacterial enteric pathogens: Campylobacter, Salmonella , Shiga-toxin producing Escherichia coli (STEC), Shigella , and Yersinia. Methods Clinical stool specimens and contrived samples comprising commonly circulating species, serotypes, biovars, and/or toxin subtypes were used for the comparison. Results Concordance rates for RT-PCR and culture using culture-positive and culture-negative clinical stools were >90% for Campylobacter (97.5-100%), Salmonella (97.5-100%), Shigella (100%), and STEC (90-100%). However, the agreement between RT-PCR and culture for Y. enteroccolitica ranged from 70-90%. For the contrived sample set, stx 2f was detected by one of four assays. Of note, no assay could detect Yersinia non- enterocolitica and Campylobacter upsaliensis. Conclusions Depending on the prevalence of certain stx sub-types, Yersinia species, and Campylobacter species in a laboratory's jurisdiction, without further improvement in culture-independent tests, culture methods remain critical for the detection of these pathogens.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".