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Record W2111081037 · doi:10.1002/jmri.22375

Imaging of lung function using hyperpolarized helium‐3 magnetic resonance imaging: Review of current and emerging translational methods and applications

2010· review· en· W2111081037 on OpenAlexafffund
Sean B. Fain, Mark L. Schiebler, David G. McCormack, Grace Párraga

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2010
Typereview
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsMedicineMagnetic resonance imagingCystic fibrosisLungAsthmaPulmonary function testingFunctional imagingMedical imagingRadiologyInternal medicine

Abstract

fetched live from OpenAlex

During the past several years there has been extensive development and application of hyperpolarized helium-3 (HP (3)He) magnetic resonance imaging (MRI) in clinical respiratory indications such as asthma, chronic obstructive pulmonary disease, cystic fibrosis, radiation-induced lung injury, and transplantation. This review focuses on the state-of-the-art of HP (3)He MRI and its application to clinical pulmonary research. This is not an overview of the physics of the method, as this topic has been covered previously. We focus here on the potential of this imaging method and its challenges in demonstrating new types of information that has the potential to influence clinical research and decision making in pulmonary medicine. Particular attention is given to functional imaging approaches related to ventilation and diffusion-weighted imaging with applications in chronic obstructive pulmonary disease, cystic fibrosis, asthma, and radiation-induced lung injury. The strengths and challenges of the application of (3)He MRI in these indications are discussed along with a comparison to established and emerging imaging techniques.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.034
GPT teacher head0.396
Teacher spread0.362 · 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 designNot applicable
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

Citations217
Published2010
Admission routes2
Has abstractyes

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