Anidulafungin is a useful surrogate marker for predicting <i>in vitro</i> susceptibility to rezafungin among five <i>Candida</i> species using CLSI methods and interpretive criteria
Bibliographic record
Abstract
ABSTRACT This study addresses the use of other echinocandins as surrogate markers to predict the susceptibility of rezafungin against the six most common Candida spp. The Clinical Laboratory Standards Institute (CLSI) reference broth microdilution method was performed to test 5,720 clinical isolates of six different Candida species. Species-specific interpretative criteria by CLSI breakpoints or epidemiological cutoff values were applied. Essential agreement was 100% within two doubling dilutions for all species and comparisons. The categorical agreement of rezafungin using anidulafungin against all Candida spp. was 97.6% (2.9% very major errors [VMEs], 0.2% major errors [MEs], and 2.2% minor errors [miEs]); for caspofungin, it was 99.6% (11.4% VME, 0.09% ME, and 0.19% miE); and for micafungin, it was 99.6% (14.3% VME, 0.15% ME, and 0.17% miE). There were species-specific differences that led to unacceptably high VME for Candida dubliniensis with all agents and for Candida parapsilosis when caspofungin or micafungin but not anidulafungin was used as the comparator. Genetic analysis showed rezafungin nonsusceptibility correlated well with FKS hotspot mutations. The best-performing surrogate was anidulafungin, which can be used to predict rezafungin susceptible or nonsusceptible in Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, and Candida krusei with low error rates and ≥90% essential and categorical agreement. Micafungin or caspofungin can also be used as a surrogate marker for predicting rezafungin susceptible or nonsusceptible in C. albicans , C. glabrata , C. tropicalis , and C. krusei . No surrogate performs appropriately to determine rezafungin susceptibility for C. dubliniensis .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".