International Evaluation of MIC Distributions and Epidemiological Cutoff Value (ECV) Definitions for Fusarium Species Identified by Molecular Methods for the CLSI Broth Microdilution Method
Bibliographic record
Abstract
The CLSI epidemiological cutoff values (ECVs) of antifungal agents are available for various Candida spp., Aspergillus spp., and the Mucorales. However, those categorical endpoints have not been established for Fusarium spp., mostly due to the difficulties associated with collecting sufficient CLSI MICs for clinical isolates identified according to the currently recommended molecular DNA-PCR-based identification methodologies. CLSI MIC distributions were established for 53 Fusarium dimerum species complex (SC), 10 F. fujikuroi, 82 F. proliferatum, 20 F. incarnatum-F. equiseti SC, 226 F. oxysporum SC, 608 F. solani SC, and 151 F. verticillioides isolates originating in 17 laboratories (in Argentina, Australia, Brazil, Canada, Europe, Mexico, and the United States). According to the CLSI guidelines for ECV setting, ECVs encompassing ≥97.5% of pooled statistically modeled MIC distributions were as follows: for amphotericin B, 4 μg/ml (F. verticillioides) and 8 μg/ml (F. oxysporum SC and F. solani SC); for posaconazole, 2 μg/ml (F. verticillioides), 8 μg/ml (F. oxysporum SC), and 32 μg/ml (F. solani SC); for voriconazole, 4 μg/ml (F. verticillioides), 16 μg/ml (F. oxysporum SC), and 32 μg/ml (F. solani SC); and for itraconazole, 32 μg/ml (F. oxysporum SC and F. solani SC). Insufficient data precluded ECV definition for the other species. Although these ECVs could aid in detecting non-wild-type isolates with reduced susceptibility to the agents evaluated, the relationship between molecular mechanisms of resistance (gene mutations) and MICs still needs to be investigated for Fusarium spp.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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".