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Record W2161004182 · doi:10.3201/eid1508.081384

Spread of <i>Cryptococcus gattii</i> into Pacific Northwest Region of the United States

2009· review· en· W2161004182 on OpenAlexaboutno aff
Kausik Datta, Karen H. Bartlett, Rebecca J. Baer, Edmond J. Byrnes, Eleni Galanis, Joseph Heitman, Linda Hoang, Mira J. Leslie, Laura MacDougall, Shelley S. Magill, Muhammad Morshed, Kieren A. Marr

Bibliographic record

VenueEmerging infectious diseases · 2009
Typereview
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCryptococcus gattiiCryptococcosisCryptococcusIncidence (geometry)DiseaseOutbreakGeographyBiologyMedicineVirologyImmunologyPathology

Abstract

fetched live from OpenAlex

Cryptococcus gattii has emerged as a human and animal pathogen in the Pacific Northwest. First recognized on Vancouver Island, British Columbia, Canada, it now involves mainland British Columbia, and Washington and Oregon in the United States. In Canada, the incidence of disease has been one of the highest worldwide. In the United States, lack of cryptococcal species identification and case surveillance limit our knowledge of C. gattii epidemiology. Infections in the Pacific Northwest are caused by multiple genotypes, but the major strain is genetically novel and may have emerged recently in association with unique mating or environmental changes. C. gattii disease affects immunocompromised and immunocompetent persons, causing substantial illness and death. Successful management requires an aggressive medical and surgical approach and consideration of potentially variable antifungal drug susceptibilities. We summarize the study results of a group of investigators and review current knowledge with the goal of increasing awareness and highlighting areas where further knowledge is required.

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.307
Teacher spread0.286 · 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

Citations270
Published2009
Admission routes1
Has abstractyes

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