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Record W4390125832 · doi:10.1002/jmri.29176

Assessment of Cerebrovascular Reactivity Using <scp>CO<sub>2</sub>‐BOLD MRI</scp>: A 15‐Year, Single Center Experience

2023· article· en· W4390125832 on OpenAlexaff
V. Raghavan, Olivia Sobczyk, Ece Su Sayin, Julien Poublanc, Abby Skanda, James Duffin, Lashmi Venkatraghavan, Joseph A. Fisher, David J. Mikulis

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of TorontoUniversity Health NetworkMcGill University
Fundersnot available
KeywordsMedicineIntraclass correlationSingle CenterBlood-oxygen-level dependentNuclear medicineMagnetic resonance imagingCerebral blood flowCardiologySurgeryRadiology

Abstract

fetched live from OpenAlex

Background Cerebrovascular reactivity (CVR) is a measure of the change in cerebral blood flow (CBF) in response to a vasoactive challenge. It is a useful indicator of the brain's vascular health. Purpose To evaluate the factors that influence successful and unsuccessful CVR examinations using precise arterial and end‐tidal partial pressure of CO 2 control during blood oxygen level–dependent (BOLD) MRI. Study Type Retrospective. Subjects Patients that underwent a CVR between October 2005 and May 2021 were studied (total of 1162 CVR examinations). The mean (±SD) age was 46.1 (±18.8) years, and 352 patients (43%) were female. Field Strength/Sequence 3 T; T1‐weighted images, T2*‐weighed two‐dimensional gradient‐echo sequence with standard echo‐planar readout. Assessment Measurements were obtained following precise hypercapnic stimuli using BOLD MRI as a surrogate of CBF. Successful CVR examinations were defined as those where: 1) patients were able to complete CVR testing, and 2) a clinically useful CVR map was generated. Unsuccessful examinations were defined as those where patients were not able to complete the CVR examination or the CVR maps were judged to be unreliable due to, for example, excessive head motion, and poor P ET CO 2 targeting. Statistical Analysis Successful and unsuccessful CVR examinations between hypercapnic stimuli, and between different patterns of stimulus were compared with Chi‐Square tests. Interobserver variability was determined by using the intraclass correlation coefficient ( P < 0.05 is significant). Results In total 1115 CVR tests in 662 patients were included in the final analysis. The success rate of generating CVR maps was 90.8% (1012 of 1115). Among the different hypercapnic stimuli, those containing a step plus a ramp protocol was the most successful (95.18%). Among the unsuccessful examinations (9.23%), most were patient related (89.3%), the most common of which was difficulty breathing. Data Conclusion CO 2 ‐BOLD MRI CVR studies are well tolerated with a high success rate. Evidence Level 4 Technical Efficacy Stage 3

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)0.002
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.328
Teacher spread0.305 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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