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Record W2370688355 · doi:10.1002/jmri.25063

Reproducibility of cerebrovascular reactivity measures in children using BOLD MRI

2015· article· en· W2370688355 on OpenAlexafffund
Jackie Leung, Junseok A. Kim, Andrea Kassner

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsReproducibilityMedicineMagnetic resonance imagingRadiologyNuclear medicineMathematicsStatistics

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the reproducibility of cerebrovascular reactivity (CVR) measurements acquired in children using magnetic resonance imaging (MRI) in combination with a computer-controlled carbon dioxide (CO2 ) stimulus. MATERIALS AND METHODS: Ten healthy children (age 16.1 ± 1.6 years) underwent CVR imaging on a 3T scanner using a blood-oxygen level-dependent (BOLD) MRI sequence. Targeted hypercapnia was induced during imaging with a CO2 gas challenge delivered using a specialized gas sequencer (RespirAct). A total of four BOLD scans were performed over 2 separate days to test within-day and between-day consistency of the data. CVR values were computed by correlating the relative change in BOLD signal in response to the CO2 stimulus delivered to the each subject. RESULTS: Intraclass correlation coefficients (ICCs) of within-day values show highly reproducible measures in both the gray matter (ICC = 0.857, P < 0.001) and white matter (ICC = 0.895, P < 0.001). Relatively lower between-day reproducibility was observed in both the gray matter (ICC = 0.776, P = 0.001) and white matter (ICC = 0.719, P = 0.004). CONCLUSION: Using a computer-controlled CO2 stimulus, we have demonstrated the reliability of BOLD-CVR measurements in pediatric subjects. Within-day and between-day metrics of reproducibility were comparable to adult data.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.167
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.033
GPT teacher head0.319
Teacher spread0.287 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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