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Cartilaginous airway wall dimensions and airway resistance in cystic fibrosis lungs

2000· article· en· W2150355155 on OpenAlexaff
Harm A.W.M. Tiddens, Laurens P. Koopman, R. K. Lambert, W. Mark Elliott, Wim C.J. Hop, T W van der Mark, W.J. de Boer, J.C. de Jongste

Bibliographic record

VenueEuropean Respiratory Journal · 2000
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCystic fibrosisAirwayCOPDAirway resistanceLung transplantationAirway obstructionLungCardiologyInternal medicinePathologySurgery

Abstract

fetched live from OpenAlex

It is not clear how airway pathology relates to the severity of airflow obstruction and increased bronchial responsiveness in cystic fibrosis (CF) patients. The aim of this study was to measure the airway dimensions of CF patients and to estimate the importance of these dimensions to airway resistance using a computational model. Airway dimensions were measured in lungs obtained from CF patients who had undergone lung transplantation (n=12), lobectomy (n=1), or autopsy (n=4). These dimensions were compared to those of airways from lobectomy specimens from 72 patients with various degrees of chronic obstructive pulmonary disease (COPD). The airway dimensions of the CF and COPD patients were introduced into a computational model to study their effect on airway resistance. The inner wall and smooth muscle areas of peripheral CF airways were increased 3.3- and 4.3-fold respectively compared to those of COPD airways. The epithelium was 53% greater in height in peripheral CF airways. The sensitivity and maximal plateau resistance of the computed dose/response curves were substantially increased in the CF patients compared to COPD patients. The changes in airway dimensions of cystic fibrosis patients probably contribute to the severe airflow obstruction, and to increased bronchial responsiveness, in these patients.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations76
Published2000
Admission routes1
Has abstractyes

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