Multicenter Evaluation of the New Vitek 2 <i>Neisseria</i> - <i>Haemophilus</i> Identification Card
Bibliographic record
Abstract
The new Neisseria-Haemophilus identification (NH) card for Vitek 2 was compared with 16S rRNA gene sequencing (16S) as the reference method for accurate identification of Neisseria spp., Haemophilus spp., and other fastidious gram-negative bacteria. Testing was performed on the Vitek 2 XL system with modified software at three clinical trial laboratories. Reproducibility was determined with nine ATCC quality control strains tested 20 times over a minimum of 10 days at all three sites. A challenge set of 30 strains with known identifications and 371 recent fresh and frozen clinical isolates were also tested. Expected positive and negative biochemical reactions were also evaluated for substrate reproducibility. All microorganisms were tested on the NH card, and all clinical and stock isolates were saved for 16S testing. All reproducibility tests yielded expected results within a 95% confidence interval. For challenge microorganisms, there was 98% overall correct identification, including 8% low discrimination, 2% incorrect identification, and 0% unidentified. For clinical strains, there was 96.5% overall correct identification, including 10.2% low discrimination, 2.7% incorrect identification, and 0.8% unidentified. The 2.7% (10/371) of clinical isolates that gave an incorrect identification consisted of 7 isolates correct to genus and 3 strains incorrect to genus. There were an additional 27 strains (primarily Neisseria species) for which the 16S identification result was different from the NH card result. These were all unclaimed species by the system. The new NH card met all performance criteria within a 95% confidence interval compared to identification of clinical isolates by 16S.
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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 | 0.039 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".