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Record W132094104 · doi:10.1055/s-2003-42378

Disorders of the Small Airways: High-Resolution Computed Tomographic Features

2003· article· en· W132094104 on OpenAlexaff
Tomás Franquet, Néstor L. Müller

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2003
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsVancouver Hospital and Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsBronchiolitisMedicineVascularityPathologyLungAirwayRespiratory diseaseBronchiolitis obliteransRespiratory systemAir trappingRadiologyComputed tomographyLung transplantationAnatomyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Several infectious and noninfectious processes may affect predominantly or exclusively the small airways and result in reversible or irreversible abnormalities. Small-airway diseases can be considered as synonymous with bronchiolitis and can be classified into three main categories: (a) obliterative (constrictive) bronchiolitis, (b) cellular bronchiolitis, and (c) respiratory bronchiolitis. The introduction of high-resolution computed tomography (HRCT) has led to a considerable improvement in our ability to diagnose small-airway diseases. The characteristic HRCT findings of obliterative bronchiolitis consist of areas of decreased attenuation and vascularity with blood flow redistribution resulting in areas of increased lung attenuation and vascularity ("mosaic perfusion" pattern). In cellular bronchiolitis, the characteristic HRCT findings consist of centrilobular nodules and branching opacities ("tree-in-bud" pattern). Finally, bilateral areas of ground-glass attenuation and/or poorly defined centrilobular nodules are characteristic of respiratory bronchiolitis and respiratory bronchiolitis-associated interstitial lung disease (RB-ILD). This article reviews the clinical, pathological, and HRCT features of some of the most common small-airway diseases.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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.

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

Citations33
Published2003
Admission routes1
Has abstractyes

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