Comparison of Accelerate PhenoTest BC Kit and MALDI-TOF MS/VITEK 2 System for the rapid identification and antimicrobial susceptibility testing of gram-negative bacilli causing bloodstream infections
Bibliographic record
Abstract
Background: Our laboratory uses matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI) and the VITEK 2 system (DV2) directly from positive blood cultures (BC) for organism identification (ID) and antimicrobial susceptibility testing (AST). Our objective was to compare direct MALDI–DV2 with a commercial BC ID–AST platform, the Accelerate Pheno system (AXDX), in the ID–AST of clinical and seeded BC positive for gram-negative bacilli (GNB). Methods: BC positive for GNB were collected over a 3-mo period and tested using AXDX and direct MALDI–DV2 and compared with conventional methods. A subset of sterile BC were seeded with multi-drug-resistant GNB. Results: Twenty-nine clinical samples and 35 seeded samples were analyzed. Direct MALDI had a higher ID failure rate (31.0%) than AXDX (3.4%; p < 0.001). Time to ID–AST was 1.5–6.9 h, 5.8–16.5 h, and 21.6–33.0 h for AXDX, direct MALDI–DV2, and conventional methods, respectively ( p < 0.001). For clinical samples, AXDX and DV2 had essential agreement (EA) or categorical agreement (CA) of more than 96%. For seeded samples, AXDX had EA, CA, VME, ME, and minor error (mE) of 93.2%, 89.0%, 2.2%, 0%, and 9.2%, respectively. AXDX had a large number of non-reports (6.1%) stemming from meropenem testing. DV2 had EA, CA, VME, ME, and mE of 97.5%, 94.7%, 1.3%, 0%, and 4.1%, respectively. Conclusions: Direct MALDI–DV2 and AXDX both had high agreement for clinical samples, but direct MALDI–DV2 had higher agreement when challenged with MDR GNB.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".