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Record W2343236106 · doi:10.1097/aci.0b013e32833363b2

Evidence for airway remodeling in chronic asthma

2010· review· en· W2343236106 on OpenAlexafffund
Tony R. Bai

Bibliographic record

VenueCurrent Opinion in Allergy and Clinical Immunology · 2010
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineAsthmaMuscle hypertrophyFibrocyteAirwayHyperplasiaLungAirway obstructionBronchoconstrictionPathologyCardiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review focuses on recent findings in relation to potential functional consequences of structural changes in the asthmatic airway. RECENT FINDINGS: Increases in smooth muscle mass have been shown to be an early finding in childhood asthma, related to clinical severity and predictive of greater airflow obstruction. Both hyperplasia and hypertrophy contribute to the increase in smooth muscle mass. A phenotypic shift in the epithelium of asthmatic airways related to stress and injury is suggested by recent data, with likely direct transformation of epithelial cells into mesenchymal cells. Fibrocyte in-migration from the vasculature may be an additional source of increased smooth muscle mass. The increased smooth muscle may contribute to neovascularization via vascular endothelial growth factor. Computed tomography studies continue to show some correlations between wall thickness and airway physiology. Exacerbations are predictive of greater lung function decline and hence remodeling. SUMMARY: On balance, recent evidence continues to show that structural changes contribute to asthma persistence, airflow obstruction, lung function decline, and clinical severity, though there is increased recognition of the heterogeneity of asthma and in some phenotypes inflammatory cell influx or vascular effects may be more important than structural effects.

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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.260
GPT teacher head0.502
Teacher spread0.242 · 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

Citations51
Published2010
Admission routes2
Has abstractyes

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