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Dynamic cerebral pressure‐flow relationships in aging and long‐term heart transplant recipients (1068.11)

2014· article· en· W1536387323 on OpenAlexafffund
Jonathan D. Smirl, Mark J. Haykowsky, Katelyn R. Marsden, Helen Jones, Michael A. Nelson, Philip N. Ainslie

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of AlbertaOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCardiologyCerebral blood flowMedicineInternal medicinePsychology

Abstract

fetched live from OpenAlex

Long‐term heart transplant recipients (HTR) provide a unique opportunity to examine the relationship between the cerebrovascular and cardiovascular systems. We examined the hypothesis that HTR would have cerebral pressure‐flow responses more comparable to control subjects matched to the age of their cerebrovasculature than their donor hearts. Eight male clinically stable HTR (62 ± 8 yrs of age and 9 ± 7 yrs post transplant), 9 male age‐matched controls (AM: 63 ± 8 yrs) and 10 male donor controls (DC: 27 ± 5 yrs), were tested. Each test involved: seated rest (5‐min), and squat‐stand maneuvers at 0.05 and 0.10 Hz. The BRS and pressure‐flow responses were assessed with TFA. BRS TFA revealed that the HTR had reductions in R‐R interval PSD and LF gain (P<0.01) compared to both control groups. There were comparable TFA cerebral pressure‐flow responses for all groups at all frequencies with coherence and phase. The pressure‐flow gain was significantly greater in DC than both AM and HTR at all frequencies (P<0.05). Cerebrovascular resistance (CVR) was negatively correlated with pressure‐flow gain at all frequencies (P<0.05). These results reveal that pressure‐flow coherence and phase are comparable throughout aging, potentially helping HTR compensate for reductions in BRS. Although pressure‐flow gain did decrease in the HTR and AM, it was related to changes in CVR highlighting the need cautiously interpret this metric. Grant Funding Source : Supported by NSERC

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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