Nomenclature for human and animal fungal pathogens and diseases: a proposal for standardized terminology
Bibliographic record
Abstract
ABSTRACT Medically important pathogenic fungi invade vertebrate tissue and are considered primary when part of their nature life cycle is associated with an animal host and are usually able to infect immunocompetent hosts. Opportunistic fungal pathogens complete their life cycle in environmental habitats or occur as commensals within or on the vertebrate body, but under certain conditions can thrive upon infecting humans. The extent of host damage in opportunistic infections largely depends on the portal and modality of entry as well as on the host’s immune and metabolic status. Diseases caused by primary pathogens and common opportunists, causing the top approximately 80% of fungal diseases [D. W. Denning, Lancet Infect Dis, 24:e428–e438, 2024, https://doi.org/10.1016/S1473-3099(23)00692-8 ], tend to follow a predictive pattern, while those by occasional opportunists are more variable. For this reason, it is recommended that diseases caused by primary pathogens and the common opportunists are named after the etiologic agent, for example, histoplasmosis and aspergillosis, while this should not be done for occasional opportunists that should be named as [causative fungus] [clinical syndrome], for example, Alternaria alternata cutaneous infection. The addition of a descriptor that identifies the location or clinical type of infection is required, as the general name alone may cover widely different clinical syndromes, for example, “rhinocerebral mucormycosis.” A list of major recommended human and animal disease entities (nomenclature) is provided in alignment with their causative agents. Fungal disease names may encompass several genera of etiologic agents, consequently being less susceptible to taxonomic changes of the causative species, for example, mucormycosis covers numerous mucormycetous molds.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".