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Record W2291379829 · doi:10.1155/2012/675743

Hyperpolarized <sup>3</sup>He Functional Magnetic Resonance Imaging of Bronchoscopic Airway Bypass in Chronic Obstructive Pulmonary Disease

2012· article· en· W2291379829 on OpenAlexafffund
Lindsay Mathew, Miranda Kirby, Donald Farquhar, Christopher Licskai, Giles Santyr, Roya Etemad‐Rezai, Grace Párraga, David G. McCormack

Bibliographic record

VenueCanadian Respiratory Journal · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineCOPDMagnetic resonance imagingPulmonary function testingObstructive lung diseaseAirwayPulmonary diseaseLungRadiologyQuality of life (healthcare)CardiologyInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

A 73-year-old exsmoker with Global initiative for chronic Obstructive Lung Disease stage III chronic obstructive pulmonary disease underwent airway bypass (AB) as part of the Exhale Airway Stents for Emphysema (EASE) trial, and was the only EASE subject to undergo hyperpolarized 3He magnetic resonance imaging for evaluation of lung function pre- and post-AB. 3He magnetic resonance imaging was acquired twice previously (32 and eight months pre-AB) and twice post-AB (six and 12 months post-AB). Six months post-AB, his increase in forced vital capacity was <12% predicted, and he was classified as an AB nonresponder. However, post-AB, he also demonstrated improvements in quality of life scores, 6 min walk distance and improvements in 3He gas distribution in the regions of stent placement. Given the complex relationship between well-established pulmonary function and quality of life measurements, the present case provides evidence of the value-added information functional imaging may provide in chronic obstructive pulmonary disease interventional studies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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 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

Citations17
Published2012
Admission routes2
Has abstractyes

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