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Record W2555121231 · doi:10.1093/mmy/myw117

<i>Aspergillus</i>in chronic lung disease: Modeling what goes on in the airways

2016· review· en· W2555121231 on OpenAlex
Takahiro Takazono, Donald C. Sheppard

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

VenueMedical Mycology · 2016
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCystic Fibrosis Foundation
KeywordsAllergic bronchopulmonary aspergillosisAspergillusImmunologyAsthmaAspergillus fumigatusAspergillosisChronic bronchitisMedicineLungDiseasePathogenSensitizationBiologyIntensive care medicineMicrobiologyPathologyImmunoglobulin EInternal medicine

Abstract

fetched live from OpenAlex

Aspergillus species cause a range of respiratory diseases in humans. While immunocompromised patients are at risk for the development of invasive infection with these opportunistic molds, patients with underlying pulmonary disease can develop chronic airway infection with Aspergillus species. These conditions span a range of inflammatory and allergic diseases including Aspergillus bronchitis, allergic bronchopulmonary aspergillosis, and severe asthma with fungal sensitization. Animal models are invaluable tools for the study of the molecular mechanism underlying the colonization of airways by Aspergillus and the host response to these non-invasive infections. In this review we summarize the state-of-the-art with respect to the available animal models of noninvasive and allergic Aspergillus airway disease; the key findings of host-pathogen interaction studies using these models; and the limitations and future directions that should guide the development and use of models for the study of these important pulmonary conditions.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.386
Teacher spread0.340 · 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