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Record W2097659543 · doi:10.1148/radiol.14140161

Hyperpolarized<sup>3</sup>He Ventilation Defects Used to Predict Pulmonary Exacerbations in Mild to Moderate Chronic Obstructive Pulmonary Disease

2014· article· en· W2097659543 on OpenAlexafffundabout
Miranda Kirby, Damien Pike, Harvey O. Coxson, David G. McCormack, Grace Párraga

Bibliographic record

VenueRadiology · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of British ColumbiaVancouver General HospitalWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineCOPDSpirometryPulmonary function testingVentilation (architecture)Vital capacityPlethysmographInternal medicineAir trappingPulmonary diseaseObstructive lung diseaseLung volumesCardiologyLungDiffusing capacityAsthmaLung function

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the predictive value of imaging and clinical and physiological measurements of chronic obstructive pulmonary disease ( COPD chronic obstructive pulmonary disease ) in patients monitored for more than 5 years for pulmonary exacerbations that required hospitalization. MATERIALS AND METHODS: Exacerbations requiring hospitalization were monitored over 5 years in 91 subjects who provided written informed consent. Study was local research ethics board and Health Canada approved and HIPAA compliant. Subjects with COPD chronic obstructive pulmonary disease underwent spirometry, plethysmography, diffusing capacity of carbon monoxide, St George's Respiratory Questionnaire, 6-minute walk test, and imaging. Computed tomographic (CT) wall area and relative area with attenuation values less than -950 HU ( RA950 relative area with attenuation values less than -950 HU ), helium 3 ((3)He) magnetic resonance (MR) imaging ventilation defect percentage ( VDP ventilation defect percentage ), and apparent diffusion coefficient were generated. Zero-inflated Poisson model was used to compare number of hospitalizations with lung function and imaging measurements. RESULTS: Twenty-four subjects were hospitalized 58 times and had significantly worse forced expiratory volume in 1 second ( FEV1 forced expiratory volume in 1 second ) (P < .0001), CT RA950 relative area with attenuation values less than -950 HU (P = .02), and (3)He VDP ventilation defect percentage (P < .0001) than values in 67 subjects who were not hospitalized. In mild to moderate COPD chronic obstructive pulmonary disease , nine hospitalized subjects had significantly worse FEV1 forced expiratory volume in 1 second (P = .02) and (3)He VDP ventilation defect percentage (P = .02) than values in 52 subjects who were not hospitalized. (3)He VDP ventilation defect percentage was quantitatively related to CT airway morphology (r = 0.26, P = .01) and quantitatively (r = 0.61, P < .0001) and spatially related to emphysema; this spatial relationship was significantly greater for hospitalized patients with COPD chronic obstructive pulmonary disease than unhospitalized patients (P = .0006). For all subjects, number of prior hospitalizations (P < .0001), 6-minute walk test distance (P < .0001), CT RA950 relative area with attenuation values less than -950 HU (P = .03), and (3)He VDP ventilation defect percentage (P = .002) were significantly related to number of hospitalizations. For 61 subjects with mild to moderate COPD chronic obstructive pulmonary disease , only (3)He VDP ventilation defect percentage was significantly associated with COPD chronic obstructive pulmonary disease exacerbations (P = .01). CONCLUSION: (3)He MR imaging VDP ventilation defect percentage represents a mixed airways-emphysema phenotype and helps identify subjects with mild to moderate COPD chronic obstructive pulmonary disease who are at risk for exacerbation that requires hospitalization.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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 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

Citations98
Published2014
Admission routes3
Has abstractyes

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