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Record W2129898448 · doi:10.1002/nbm.1470

Comparison of hyperpolarized <sup>3</sup>He MRI rat lung volume measurement with micro‐computed tomography

2010· article· en· W2129898448 on OpenAlexafffund
Mohammad Reza Sharif, Wilfred Lam, Alexei Ouriadov, David W. Holdsworth, Giles Santyr

Bibliographic record

VenueNMR in Biomedicine · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsRobarts Clinical TrialsHealth Sciences CentreWestern UniversitySunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsComputed tomographyNuclear magnetic resonanceNuclear medicineMagnetic resonance imagingVolume (thermodynamics)ChemistryPhysicsMaterials scienceMedicineRadiology

Abstract

fetched live from OpenAlex

In this study, the upper-limit volume (gas plus partial tissue volume) as well as absolute volume (gas only) of lungs measured with hyperpolarized (3)He-MR imaging is compared with that determined by micro-computed tomography (CT) under similar ventilation conditions in normal rats. Five Brown Norway rats (210-259 g) were ventilated with O(2), alternately with (3)He, using a computer-controlled ventilator, and 3D density-weighted images of the lungs were acquired during a breath hold after six wash-in breaths of (3)He. The rats were then transferred to a micro-CT scanner, and a similar experimental setup was used to obtain images of the lungs during a breath hold of air with an airway pressure equal to that of the MR imaging breath hold. The upper-limit and absolute volumes obtained from (3)He-MR and micro-CT methods were not significantly different (p > 0.05). The good agreement between the lung volumes measured with the two imaging methods suggests that (3)He-MR imaging can be used for quantitative analysis of lung volume changes in longitudinal studies without the exposure to the ionizing radiation which accompanies micro-CT imaging.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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.

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

Citations9
Published2010
Admission routes2
Has abstractyes

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