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Record W2330494306 · doi:10.1097/mnm.0b013e328355d8bc

Uniformity and repeatability of normal resting myocardial blood flow in rats using [13N]-ammonia and small animal PET

2012· article· en· W2330494306 on OpenAlexafffund
Marc Lamoureux, Stephanie Thorn, Tyler Dumouchel, Jennifer M. Renaud, Ran Klein, Samantha Mason, Mireille Lortie, Jean N. DaSilva, Rob Beanlands, Robert A. deKemp

Bibliographic record

VenueNuclear Medicine Communications · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsRepeatabilityBlood flowPerfusionPopulationMedicineCoefficient of variationNuclear medicineCardiologyInternal medicineChemistryChromatography

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to quantitatively evaluate population variability, regional uniformity and repeatability of myocardial blood flow measurements using [13N]-ammonia and small animal PET. METHODS: Serial PET scans were conducted on Sprague-Dawley rats using [13N]-ammonia to study relative perfusion and absolute myocardial blood flow (ml/min/g). FlowQuant automated analysis software was used to produce five-segment polar maps to investigate regional myocardial blood flow differences. The interobserver and intraobserver repeatability was assessed quantitatively using Bland-Altman analysis. RESULTS: Absolute myocardial blood flow values were 4.3 ± 1.1 ml/min/g, corresponding to a population variability of 25.5%. There were significant age-related increases in resting myocardial blood flow (r2=0.59, P<0.001). The test-retest differences had a coefficient of repeatability of 24.5% of the mean myocardial blood flow. The operator variability was small, relative to the population variability. CONCLUSION: Repeatable myocardial blood flow values are minimally influenced by operator intervention. However, age-related myocardial blood flow increases must be taken into account when comparing measurements between experimental groups.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.021
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.312
Teacher spread0.259 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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