Posaconazole MIC Distributions for Aspergillus fumigatus Species Complex by Four Methods: Impact of cyp51A Mutations on Estimation of Epidemiological Cutoff Values
Bibliographic record
Abstract
Estimating epidemiological cutoff endpoints (ECVs/ECOFFS) may be hindered by the overlap of MICs for mutant and nonmutant strains (strains harboring or not harboring mutations, respectively). Posaconazole MIC distributions for the Aspergillus fumigatus species complex were collected from 26 laboratories (in Australia, Canada, Europe, India, South and North America, and Taiwan) and published studies. Distributions that fulfilled CLSI criteria were pooled and ECVs were estimated. The sensitivity of three ECV analytical techniques (the ECOFFinder, normalized resistance interpretation [NRI], derivatization methods) to the inclusion of MICs for mutants was examined for three susceptibility testing methods (the CLSI, EUCAST, and Etest methods). The totals of posaconazole MICs for nonmutant isolates (isolates with no known cyp51A mutations) and mutant A. fumigatus isolates were as follows: by the CLSI method, 2,223 and 274, respectively; by the EUCAST method, 556 and 52, respectively; and by Etest, 1,365 and 29, respectively. MICs for 381 isolates with unknown mutational status were also evaluated with the Sensititre YeastOne system (SYO). We observed an overlap in posaconazole MICs among nonmutants and cyp51A mutants. At the commonly chosen percentage of the modeled wild-type population (97.5%), almost all ECVs remained the same when the MICs for nonmutant and mutant distributions were merged: ECOFFinder ECVs, 0.5 μg/ml for the CLSI method and 0.25 μg/ml for the EUCAST method and Etest; NRI ECVs, 0.5 μg/ml for all three methods. However, the ECOFFinder ECV for 95% of the nonmutant population by the CLSI method was 0.25 μg/ml. The tentative ECOFFinder ECV with SYO was 0.06 μg/ml (data from 3/8 laboratories). Derivatization ECVs with or without mutant inclusion were either 0.25
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".