Multicenter Clinical Validation of the Molecular BD Max Enteric Viral Panel for Detection of Enteric Pathogens
Bibliographic record
Abstract
The conventional methodology for gastrointestinal pathogen detection remains time-consuming, expensive, and of limited sensitivity. The objective of this study was to evaluate the performance of the BD Max enteric viral panel (Max EVP) assay for identification of viral pathogens in stool specimens from individuals with symptoms of acute gastroenteritis, enteritis, or colitis. Prospective and archival stool specimens from adult and pediatric patients with diarrhea were collected in Cary-Blair medium or unpreserved containers. The results for specimens tested by the Max EVP (on the BD Max platform) were compared to those obtained by the reference method (alternate PCR assays, followed by bidirectional sequencing). Positive percent agreement (PPA) and negative percent agreement (NPA) were calculated. A total of 2,239 specimens were collected, with 2,148 being included for analysis. In this population, 39.6% of specimens were from outpatients, 42.1% were from patients <21 years old, and 49.7% were from females. Prevalence rates for prospective specimens were 7.3%, 4.5%, 3.5%, 2.4%, and 1.2% for norovirus, sapovirus, astrovirus, rotavirus, and adenovirus, respectively. PPA was 92.8%, 84.9%, 93.0%, 100%, and 95.6%, for norovirus, sapovirus, astrovirus, rotavirus, and adenovirus, respectively. NPA was ≥99.4% for all targets. In conjunction with the clinical presentation, laboratory findings, and epidemiological information, the Max EVP assay is effective for the differential diagnosis of enteric disease caused by norovirus, sapovirus, astrovirus, rotavirus, and adenovirus. This assay can be used individually for patients at high risk for a viral enteropathogen (e.g., in outbreak settings) or as an adjunct to other enteric bacterial panels.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".