Multi-Year Comparison of VITEK® MS performance for identification of rarely encountered pathogenic gram-negative bacilli (GNBs) in a large integrated Canadian healthcare region.
Bibliographic record
Abstract
Background: This multi-year study (2014-19) compared identification of rare and unusual GNB by MALDI-TOF MS (VITEK® MS, bioMérieux, Laval Que.) to 16S rRNA gene sequencing (16S) according to our laboratories routine workflow; 16S is done if initial MALDI-TOF MS results were discordant, wrong or absent. Materials and Methods: GNB isolates were first analyzed by standard phenotypic methods and MALDI-TOF MS using direct deposit with full formic acid extraction; proteomics was repeated if no result occurred. Medically approved 16S analyses were done using fast protocols. Isolate sequences were analyzed using IDNS3 bacterial database (SmartGene™, Lausanne, Switzerland). Results: 329 GNB isolates were recovered from 304 specimens; >1 isolate was recovered from 19(6%). 250(76%) NFGNBs, 62(19%) fGNBs, and 17(5%) CAMPB were mainly recovered from blood cultures (31.6%) and lower respiratory specimens (43%) (one-half were isolated from cystic fibrosis patients). Accurate genus vs. species identities were obtained for 67.2%/26% NFGNBs, 74.2%/53.2% fGNBs, and 22% CAMPB (with no discrepant species), respectively. Wrong or no results were obtained for 82(32.8%) NFGNB, 17(27.4%) fGNB, and 13(72.2%) CAMPB. Absent or misidentifications occurred for NFGNBs (33%), fGNBs (26%) and CAMPB (89%) due to absent species in the instrument’s database. VITEK MS performance remained stable for NFGNBs and fGNBs but improved for CAMPB but with the addition of Campylobacter rectus and Campylobacter curvus to the database. Conclusions: VITEK® MS databases need to be continually updated to include an increasing number of rare and unusual GNBs causing invasive human infections. 16S remains important for GNB identification where proteomics fails.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".