Melting curve analysis reveals false-positive norovirus detection in a molecular syndromic panel
Bibliographic record
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 higher than expected number of stool samples with norovirus were detected. OBJECTIVES: The goal of this study was 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"). Furthermore, 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 detected by BioFire including norovirus 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 mitigate potential false-positive norovirus results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".