Multilaboratory Study of Epidemiological Cutoff Values for Detection of Resistance in Eight Candida Species to Fluconazole, Posaconazole, and Voriconazole
Bibliographic record
Abstract
Although epidemiological cutoff values (ECVs) have been established for Candida spp. and the triazoles, they are based on MIC data from a single laboratory. We have established ECVs for eight Candida species and fluconazole, posaconazole, and voriconazole based on wild-type (WT) MIC distributions for isolates of C. albicans (n=11,241 isolates), C. glabrata (7,538), C. parapsilosis (6,023), C. tropicalis (3,748), C. krusei (1,073), C. lusitaniae (574), C. guilliermondii (373), and C. dubliniensis (162). The 24-h CLSI broth microdilution MICs were collated from multiple laboratories (in Canada, Brazil, Europe, Mexico, Peru, and the United States). The ECVs for distributions originating from ≥6 laboratories, which included ≥95% of the modeled WT population, for fluconazole, posaconazole, and voriconazole were, respectively, 0.5, 0.06 and 0.03 μg/ml for C. albicans, 0.5, 0.25, and 0.03 μg/ml for C. dubliniensis, 8, 1, and 0.25 μg/ml for C. glabrata, 8, 0.5, and 0.12 μg/ml for C. guilliermondii, 32, 0.5, and 0.25 μg/ml for C. krusei, 1, 0.06, and 0.06 μg/ml for C. lusitaniae, 1, 0.25, and 0.03 μg/ml for C. parapsilosis, and 1, 0.12, and 0.06 μg/ml for C. tropicalis. The low number of MICs (<100) for other less prevalent species (C. famata, C. kefyr, C. orthopsilosis, C. rugosa) precluded ECV definition, but their MIC distributions are documented. Evaluation of our ECVs for some species/agent combinations using published individual MICs for 136 isolates (harboring mutations in or upregulation of ERG11, MDR1, CDR1, or CDR2) and 64 WT isolates indicated that our ECVs may be useful in distinguishing WT from non-WT isolates.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".