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Record W2329579756 · doi:10.14814/phy2.12761

Hyperpolarized<sup>3</sup>He magnetic resonance imaging ventilation defects in asthma: relationship to airway mechanics

2016· article· en· W2329579756 on OpenAlexafffund
Del Leary, Sarah Svenningsen, Fumin Guo, Swati A. Bhatawadekar, Grace Párraga, Geoffrey N. Maksym

Bibliographic record

VenuePhysiological Reports · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsDalhousie UniversityToronto Rehabilitation InstituteUniversity Health NetworkWestern University
FundersCanadian Institutes of Health ResearchAtlantic Canada Opportunities AgencyCanadian Lung AssociationCanadian Thoracic Society
KeywordsVentilation (architecture)Airway resistanceMedicineMagnetic resonance imagingRespiratory physiologyMechanical ventilationAsthmaRespiratory systemAirwayCardiologyPlethysmographLungTidal volumeReactanceInternal medicineAnesthesiaRadiologyPhysics

Abstract

fetched live from OpenAlex

In patients with asthma, magnetic resonance imaging (MRI) provides direct measurements of regional ventilation heterogeneity, the etiology of which is not well-understood, nor is the relationship of ventilation abnormalities with lung mechanics. In addition, respiratory resistance and reactance are often abnormal in asthmatics and the frequency dependence of respiratory resistance is thought to reflect ventilation heterogeneity. We acquiredMRIventilation defect maps, forced expiratory volume in one-second (FEV1), and airways resistance (Raw) measurements, and used a computational airway model to explore the relationship of ventilation defect percent (VDP) with simulated measurements of respiratory system resistance (Rrs) and reactance (Xrs).MRIventilation defect maps were experimentally acquired in 25 asthmatics before, during, and after methacholine challenge and these were nonrigidly coregistered to the airway tree model. Using the model coregistered to ventilation defect maps, we narrowed proximal (9th) and distal (14th) generation airways that were spatially related to theMRIventilation defects. The relationships forVDPwith Raw measured using plethysmography (r = 0.79), and model predictions of Rrs>14(r = 0.91,P < 0.0001) and Rrs>9(r = 0.88,P < 0.0001) were significantly stronger (P = 0.005;P = 0.03, respectively) than withFEV1(r = -0.68,P = 0.0001). The slopes for the relationship ofVDPwith simulated lung mechanics measurements were different (P < 0.0001); among these, the slope for theVDP-Xrs0.2relationship was largest, suggesting thatVDPwas dominated by peripheral airway heterogeneity in these patients. In conclusion, as a first step toward understanding potential links between lung mechanics and ventilation defects, impedance predictions were made using a computational airway tree model with simulated constriction of airways related to ventilation defects measured in mild-moderate asthmatics.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.276
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 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

Citations24
Published2016
Admission routes2
Has abstractyes

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