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Record W2804198042 · doi:10.1111/nyas.13739

New pathogens, new tricks: emerging, drug‐resistant fungal pathogens and future prospects for antifungal therapeutics

2018· review· en· W2804198042 on OpenAlexaff
Jennifer Geddes‐McAlister, Rebecca S. Shapiro

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2018
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAntifungalAntifungal drugsAntifungal drugDrug resistanceDrugMicrobiologyBiologyMedicinePharmacology

Abstract

fetched live from OpenAlex

Fungal pathogens are a growing threat to public health. As human immunodeficiency becomes increasingly common, fungal infections are becoming more prevalent. The use of antifungal agents for prophylaxis and treatment of fungal infections has favored the emergence of previously rare or unidentified species of drug-resistant fungal pathogens, including several Candida and Cryptococcus species, as well as mold pathogens. As these new and increasingly drug-resistant fungal pathogens continue to emerge, novel strategies for rapid identification and treatment are necessary to combat these life-threatening infections.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.123
GPT teacher head0.392
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations166
Published2018
Admission routes1
Has abstractyes

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