Diagnosis of Breakthrough Fungal Infections in the Clinical Mycology Laboratory: An ECMM Consensus Statement
Bibliographic record
Abstract
Breakthrough invasive fungal infections (bIFI) cause significant morbidity and mortality. Their diagnosis can be challenging due to reduced sensitivity to conventional culture techniques, serologic tests, and PCR-based assays in patients undergoing antifungal therapy, and their diagnosis can be delayed contributing to poor patient outcomes. In this review, we provide consensus recommendations on behalf of the European Confederation for Medical Mycology (ECMM) for the diagnosis of bIFI caused by invasive yeasts, molds, and endemic mycoses, to guide diagnostic efforts in patients receiving antifungals and support the design of future clinical trials in the field of clinical mycology. The cornerstone of lab-based diagnosis of breakthrough infections for yeast and endemic mycoses remain conventional culture, to accurately identify the causative pathogen and allow for antifungal susceptibility testing. The impact of non-culture-based methods are not well-studied for the definite diagnosis of breakthrough invasive yeast infections. Non-culture-based methods have an important role for the diagnosis of breakthrough invasive mold infections, in particular invasive aspergillosis, and a combination of testing involving conventional culture, antigen-based assays, and PCR-based assays should be considered. Multiple diagnostic modalities, including histopathology, culture, antibody, and/or antigen tests and occasionally PCR-based assays may be required to diagnose breakthrough endemic mycoses. A need exists for diagnostic tests that are effective, simple, cheap, and rapid to enable the diagnosis of bIFI in patients taking antifungals.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".