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Record W3005239418 · doi:10.1007/s11046-020-00431-2

Re-drawing the Maps for Endemic Mycoses

2020· review· en· W3005239418 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMycopathologia · 2020
Typereview
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeMedical Research Council
KeywordsParacoccidioidomycosisHistoplasmosisBlastomycosisEpidemiologyEndemic diseaseDiseaseMedical microbiologyEndemic diseasesFungal diseaseMedicineGeographyImmunologyDermatologyEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Endemic mycoses such as histoplasmosis, coccidioidomycosis, blastomycosis, paracoccidioidomycosis, and talaromycosis are well-known causes of focal and systemic disease within specific geographic areas of known endemicity. However, over the past few decades, there have been increasingly frequent reports of infections due to endemic fungi in areas previously thought to be "non-endemic." There are numerous potential reasons for this shift such as increased use of immune suppressive medications, improved diagnostic tests, increased disease recognition, and global factors such as migration, increased travel, and climate change. Regardless of the causes, it has become evident that our previous understanding of endemic regions for these fungal diseases needs to evolve. The epidemiology of the newly described Emergomyces is incomplete; our understanding of it continues to evolve. This review will focus on the evidence underlying the established areas of endemicity for these mycoses as well as new data and reports from medical literature that support the re-thinking these geographic boundaries. Updating the endemic fungi maps would inform clinical practice and global surveillance of these diseases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.914
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.108
GPT teacher head0.376
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it