MétaCan
Menu
Back to cohort
Record W2092274640 · doi:10.1097/mcp.0000000000000120

Therapeutic implications of ‘neutrophilic asthma’

2014· review· en· W2092274640 on OpenAlexaff
Parameswaran Nair, Afia Aziz-Ur-Rehman, Katherine Radford

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersGaldermaTeva Pharmaceutical IndustriesSanofi
KeywordsNeutrophiliaMedicineAsthmaAirwayImmunologyAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review examines the association between airway neutrophilia and severe asthma, potential mechanisms, and the effect on asthma control of therapies directed at reducing airway neutrophil numbers or activity. RECENT FINDINGS: The majority of studies that observe an association between airway neutrophilia and severe asthma are cross-sectional in nature, and the intensity of neutrophilia is low and may be a reflection of the age of the patients, effect of tobacco smoke exposure, or the high doses of corticosteroids used to treat their asthma. There may be a small proportion of patients who may have abnormal innate immune responses that may lead to airway neutrophilia. However, these neutrophils may not be any more activated than in patients with milder asthma. Novel strategies using small molecule antagonists against the interleukin-8 receptor, CXCR2, are able to reduce airway neutrophilia, and their clinical efficacies are being investigated. SUMMARY: Although cross-sectional studies suggest that airway neutrophilia may be observed in some patients with severe asthma, it is not clearly established if this is a consequence of treatment with corticosteroids or if it contributes directly to asthma pathobiology and severity. New therapies such as anti-CXCR2 provide an opportunity to investigate the contribution of neutrophils to asthma severity.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
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 head0.116
GPT teacher head0.428
Teacher spread0.311 · 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 teacher head, not a consensus.

Study designOther design
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

Citations54
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Pulmonary MedicineSame topicAsthma and respiratory diseasesFrench-language works237,207