Multicenter Study of Anidulafungin and Micafungin MIC Distributions and Epidemiological Cutoff Values for Eight Candida Species and the CLSI M27-A3 Broth Microdilution Method
Bibliographic record
Abstract
Since epidemiological cutoff values (ECVs) using CLSI MICs from multiple laboratories are not available for Candida spp. and the echinocandins, we established ECVs for anidulafungin and micafungin on the basis of wild-type (WT) MIC distributions (for organisms in a species-drug combination with no detectable acquired resistance mechanisms) for 8,210 Candida albicans, 3,102 C. glabrata, 3,976 C. parapsilosis, 2,042 C. tropicalis, 617 C. krusei, 258 C. lusitaniae, 234 C. guilliermondii, and 131 C. dubliniensis isolates. CLSI broth microdilution MIC data gathered from 15 different laboratories in Canada, Europe, Mexico, Peru, and the United States were aggregated to statistically define ECVs. ECVs encompassing 97.5% of the statistically modeled population for anidulafungin and micafungin were, respectively, 0.12 and 0.03 μg/ml for C. albicans, 0.12 and 0.03 μg/ml for C. glabrata, 8 and 4 μg/ml for C. parapsilosis, 0.12 and 0.06 μg/ml for C. tropicalis, 0.25 and 0.25 μg/ml for C. krusei, 1 and 0.5 μg/ml for C. lusitaniae, 8 and 2 μg/ml for C. guilliermondii, and 0.12 and 0.12 μg/ml for C. dubliniensis. Previously reported single and multicenter ECVs defined in the present study were quite similar or within 1 2-fold dilution of each other. For a collection of 230 WT isolates (no fks mutations) and 51 isolates with fks mutations, the species-specific ECVs for anidulafungin and micafungin correctly classified 47 (92.2%) and 51 (100%) of the fks mutants, respectively, as non-WT strains. These ECVs may aid in detecting non-WT isolates with reduced susceptibility to anidulafungin and micafungin due to fks mutations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".