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Record W2991525627 · doi:10.4187/respcare.06697

Cystic Fibrosis Lung Disease: An Overview

2019· review· en· W2991525627 on OpenAlexfundno aff
Nelson L. Turcios

Bibliographic record

VenueRespiratory Care · 2019
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
FundersNational Institutes of HealthYork University
KeywordsMedicineCystic fibrosisBronchiectasisLungMucusLung transplantationDiseaseRespiratory diseaseMucociliary clearancePathologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Although better insights into the natural course of cystic fibrosis (CF) have led to treatment approaches that have improved pulmonary health and increased the life expectancy of individuals with this disorder, lung disease remains the main cause of morbidity and mortality in patients with CF. Evidence suggests that airway epithelial defects in ions-water transport lead to dehydrated mucus, impaired mucus clearance, and mucus adhesion to airway surfaces. An increase in mucin secretion is also suggested by the formation of endobronchial mucus plaques and plugs, which become the main sites of air flow obstruction, infection, and inflammation conducing to early small airways disease followed by the development of bronchiectasis. The lung involvement is usually progressive with intermittent exacerbations. Aggressive management and advances in treatment delay, but, do not prevent progression of lung disease. Respiratory failure ensues and is the major cause of death. The lung parenchyma is virtually untouched for much of the course of the disease. This review focuses on the lung involvement in cystic fibrosis and summarizes new developments on the diagnostic approach of CF and pathogenesis of related lung disease. Current therapeutic modalities, novel therapies targeting the basic genetic defect, and lung transplantation are also reviewed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.110
GPT teacher head0.440
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations212
Published2019
Admission routes1
Has abstractyes

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