Melting curve analysis reveals false-positive norovirus detection in a molecular syndromic panel
Bibliographic record
Abstract
Abstract Background Molecular syndromic panels can improve rapidity of results and ease clinical laboratory workflow, although caution has been raised for potential false-positive results. Upon implementation of a new panel for infectious diarrhea (BioFire® FilmArray® Gastrointestinal [GI] Panel, bioMérieux) in our clinical laboratory, a high number of stool samples with norovirus were detected. Objectives To investigate positive percent agreement and the false-positive rate of norovirus detected by the multiplex Biofire GI panel compared to a singleplex commercial assay. Study design From October 2023 to January 2024, all prospective stool samples with a positive norovirus result by BioFire had melting curves reviewed manually using the BioFire FilmArray Torch System. Stool samples further underwent testing by a supplementary real-time RT-PCR assay (Xpert® Norovirus, Cepheid) for comparative analysis. Results Of the 50 stool samples with norovirus detected by BioFire, 18 (36%) tested negative by Xpert (deemed “false-positives”). Melting curve analysis revealed nearly all of these samples had atypical melting curve morphologies for the “Noro-1” target on BioFire (16/18, 89%), which was statistically significant (Odds Ratio 173.2, 95% CI [22.2, 5326.9], p <0.0001). Stool samples with multiple pathogens including norovirus detected by BioFire were not more likely to produce false-positive norovirus results (Odds Ratio 1, 95% CI [0.3, 3.3], p =1). Conclusions Although not described in the manufacturer’s Instructions for Use , we propose routine manual review of melting curves for the BioFire GI panel prior to reporting, to avoid potential false-positive norovirus results.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".