A conceptual framework for nomenclatural stability and validity of medically important fungi: a proposed global consensus guideline for fungal name changes supported by ABP, ASM, CLSI, ECMM, ESCMID-EFISG, EUCAST-AFST, FDLC, IDSA, ISHAM, MMSA, and MSGERC
Bibliographic record
Abstract
ABSTRACT The rapid pace of name changes of medically important fungi is creating challenges for clinical laboratories and clinicians involved in patient care. We describe two sources of name change which have different drivers, at the species versus the genus level. Some suggestions are made here to reduce the number of name changes. We urge taxonomists to provide diagnostic markers of taxonomic novelties. Given the instability of phylogenetic trees due to variable taxon sampling, we advocate to maintain genera at the largest possible size. Reporting of identified species in complexes or series should where possible comprise both the name of the overarching species and that of the molecular sibling, often cryptic species. Because the use of different names for the same species will be unavoidable for many years to come, an open access online database of the names of all medically important fungi, with proper nomenclatural designation and synonymy, is essential. We further recommend that while taxonomic discovery continues, the adaptation of new name changes by clinical laboratories and clinicians be reviewed routinely by a standing committee for validation and stability over time, with reference to an open access database, wherein reasons for changes are listed in a transparent way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.409 | 0.325 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.014 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.030 | 0.021 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".