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Record W2017735 · doi:10.1155/2011/354325

Normal Expiratory Flow Rate and Lung Volumes in Patients with Combined Emphysema and Interstitial Lung Disease: A Case Series and Literature Review

2011· review· en· W2017735 on OpenAlexaff
Karen L Heathcote, Donald W. Cockcroft, D. Fladeland, Mark Fenton

Bibliographic record

VenueCanadian Respiratory Journal · 2011
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsMedicineAir trappingElastic recoilLungSpirometryLung volumesIdiopathic pulmonary fibrosisPulmonary function testingCardiologyDiffusing capacityExtrinsic Allergic AlveolitisInternal medicineLung functionAsthma

Abstract

fetched live from OpenAlex

Pulmonary function tests in patients with idiopathic pulmonary fibrosis characteristically show a restrictive pattern including small lung volumes and increased expiratory flow rates resulting from a reduction in pulmonary compliance due to diffuse fibrosis. Conversely, an obstructive pattern with hyperinflation results in emphysema by loss of elastic recoil, expiratory collapse of the peripheral airways and air trapping. When the diseases coexist, pulmonary volumes are compensated, and a smaller than expected reduction or even normal lung volumes can be found. The present report describes 10 patients with progressive breathlessness, three of whom experienced severe limitation in their quality of life. All patients showed lung interstitial involvement and emphysema on computed tomography scan of the chest. The 10 patients showed normal spirometry and lung volumes with severe compromise of gas exchange. Normal lung volumes do not exclude diagnosis of idiopathic pulmonary fibrosis in patients with concomitant emphysema. The relatively preserved lung volumes may underestimate the severity of idiopathic pulmonary fibrosis and attenuate its effects on lung function parameters.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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 designSystematic review
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

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

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