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Record W2470078412 · doi:10.1016/j.nicl.2016.05.008

Impaired dynamic cerebrovascular response to hypercapnia predicts development of white matter hyperintensities

2016· article· en· W2470078412 on OpenAlexafffund
Kevin Sam, John Conklin, Kenneth R. Holmes, Olivia Sobczyk, Julien Poublanc, Adrian P. Crawley, Daniel M. Mandell, Lashmi Venkatraghavan, James Duffin, Joseph A. Fisher, Sandra E. Black, David J. Mikulis

Bibliographic record

VenueNeuroImage Clinical · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoHealth Sciences CentreUniversity Health Network
FundersCanadian Stroke NetworkOntario Research Foundation
KeywordsWhite matterHyperintensityCardiologyLeukoaraiosisInternal medicineMedicineMagnetic resonance imagingNeurosciencePsychologyRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the relationship between both dynamic and steady-state measures of cerebrovascular reactivity (CVR) and the progression of age-related white matter disease. METHODS: Blood oxygen level-dependent (BOLD) MRI CVR scans were acquired from forty-five subjects (age range: 50-90 years, 25 males) with moderate to severe white matter disease, at baseline and one-year follow-up. To calculate the dynamic (τ) and steady-state (ssCVR) components of the BOLD signal response, the PETCO2 signal waveform was convolved with an exponential decay function. The τ corresponding to the best fit between the convolved PETCO2 and BOLD signal defined the speed of response, and the slope of the regression between the convolved PETCO2 and BOLD signal defined ssCVR. ssCVR and τ were compared between normal-appearing white matter (NAWM) that remains stable over time and NAWM that progresses to white matter hyperintensities (WMHs). RESULTS: In comparison to contralateral NAWM, NAWM that progressed to WMH had significantly lower ssCVR values by mean (SD) 46.5 (7.6)%, and higher τ values by 31.9 (9.6)% (both P < 0.01). CONCLUSIONS: Vascular impairment in regions of NAWM that progresses to WMH consists not only of decreased magnitude of ssCVR, but also a pathological decrease in the speed of vascular response. These findings support the association between cerebrovascular dysregulation and the development of WMH.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.040
GPT teacher head0.361
Teacher spread0.321 · 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

Citations54
Published2016
Admission routes2
Has abstractyes

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