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Record W2766804427 · doi:10.1097/mcp.0000000000000441

Airway remodeling in asthma

2017· review· en· W2766804427 on OpenAlexaff
Louis‐Philippe Boulet

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversité LavalInstitut Universitaire de Cardiologie et de Pneumologie de Québec
FundersSanofiAstraZenecaAmgen
KeywordsBronchial thermoplastyMedicineAirwayAsthmaExtracellular matrixInflammationImmunologyCell biologyBronchoconstrictionBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The term 'airway remodeling' reflects changes in the type, quantity, and nature of airway wall components and their organization. The purpose of this review is to look at recent publications on airway remodeling in asthma. RECENT FINDINGS: Animal models and in-vitro studies have confirmed the involvement of airway epithelium, airway smooth muscle (ASM), and extracellular matrix components in asthma-related airway remodeling. They report influences on proliferation of ASM cells, and how their orientation or morphology, in addition to the heterogeneity of ASM mass at different levels of airways could influence their effects. Clinical benefits have been observed following reduction of ASM following bronchial thermoplasty. Asthmatic epithelial cell transcriptome alterations were found to involve metabolism and epigenetics, beyond epithelial mesenchymal trophic unit driven by injury and repair in chronic inflammation. New ways to explore airway remodeling such as imaging or endoscopic techniques have been evaluated. Finally, new data support the role of eosinophils and mast cells in remodeling and show the influence of new asthma drugs on this process. SUMMARY: As recently stated by an American Thoracic Society task force, we need more research on airway remodeling, its determinants and clinical relevance, and on the effects of asthma drugs on its various components.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.242
GPT teacher head0.472
Teacher spread0.231 · 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

Citations173
Published2017
Admission routes1
Has abstractyes

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