Establishing piperacillin–tazobactam susceptibility in ceftriaxone non-susceptible Enterobacterales: comparing disk diffusion, Etest, and VITEK 2 automated minimal inhibitory concentration measurements vs. broth microdilution
Bibliographic record
Abstract
OBJECTIVES: Post-hoc analyses of the MERINO trial highlight the uncertainty associated with establishing piperacillin-tazobactam (PTZ) susceptibility in extended-spectrum beta-lactamase-producing Enterobacterales. Herein, we compare the concordance of susceptibility for PTZ among the VITEK 2, disc diffusion, and Etest with broth microdilution (BMD) as the reference standard. METHODS: Ninety-four consecutive ceftriaxone non-susceptible Escherichia coli and Klebsiella pneumoniae bloodstream isolates were identified from patients at three hospitals in Montréal, Québec. BMD was used as the reference standard against which disc diffusion, VITEK 2 (AST-N391), and Etest susceptibility testing were compared. Errors were categorized as very major (false susceptible), major (false resistant), and minor (other). RESULTS: Overall, 68/94 (72.3%) of isolates were susceptible to PTZ by BMD. Disc diffusion made no major or very major errors (0%; 97.5% CI: 0-3.8%). The VITEK 2 system had a major error rate of 2.5% (95% CI: 0.003-0.089%) and a very major error rate of 26.7% (95% CI: 0.08-0.55%); however, all isolates with VITEK 2 minimal inhibitory concentrations (MICs) of ≤4 μg/mL were susceptible. Finally, the Etest had a major error rate of 6.3% (95% CI: 0.02-0.14%), but no very major errors. Combining VITEK 2-determined susceptibility with a second test led to an increase in the number of correctly classified susceptible organisms. DISCUSSION: The VITEK 2 system, and to a lesser extent the Etest, risk major errors. Used alone, the VITEK 2 system also risks very major errors if the estimated MIC is > 4 μg/mL. Combining VITEK 2 with disc diffusion in isolates with an estimated MIC of 8-16 μg/mL could prevent both major and very major errors.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".