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Record W2957444086 · doi:10.1111/all.13985

Mechanisms and therapeutic strategies for non‐T2 asthma

2019· review· en· W2957444086 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

VenueAllergy · 2019
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsNeutrophiliaMedicineAsthmaSputumEndotypeAirwayImmunologyAirway hyperresponsivenessBronchodilatorAllergyIntensive care medicinePathologyAnesthesia

Abstract

fetched live from OpenAlex

Non-T2 asthma is traditionally defined as asthma without features of T2 asthma. The definition is arbitrary and is generally based on the presence of neutrophils in sputum, or the absence (or normal levels) of eosinophils or other T2 markers in sputum (paucigranulocytic), airway biopsies or in blood. This definition may be imprecise as we gain more knowledge from applying transcriptomics and proteomics to blood and airway samples. The prevalence of non-T2 asthma is also difficult to estimate as most studies are cross-sectional and influenced by concomitant treatment with glucocorticosteroids, and by the presence of recognized or unrecognized airway infections. No specific therapies have shown any clinical benefits in patients with asthma that is associated with a non-T2 inflammatory process. It remains to be seen if such an endotype truly exists and to identify treatments to target that endotype. Meanwhile, identifying intense airway neutrophilia as an indicator of airway infection and airway hyperresponsiveness as an indicator of smooth muscle dysfunction, and treating them appropriately, and not increasing glucocorticosteroids in patients who do not have obvious T2 inflammation, seem reasonable.

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)0.000
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 score0.993
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.340
Teacher spread0.288 · 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