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Cardio‐postural interactions and aging

2008· article· en· W2280515457 on OpenAlexafffund
Andrew P. Blaber, Clint Landrock, Philippe A. Souvestre

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsProvidence Health CareSimon Fraser University
FundersSimon Fraser University
KeywordsBlood pressureBaroreflexSittingMedicineCardiologyHeart ratePhysical medicine and rehabilitationPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Until recently cardiovascular and postural reflexes have been looked at as independent control systems. In this study we propose an interaction between these two systems, particularly in relation to the maintenance of venous return through skeletal muscle pump in conditions of impaired vascular control or increased venous pooling. We investigated this “cardio‐postural” interaction with aging. Methods: Healthy elderly and young subjects were tested in a sit to stand test. Mediolateral centre of pressure (COP) trajectory, as well as non‐invasive heart rate and blood pressure data were collected in two test conditions (eyes open or eyes closed) in randomized order. Data was collected in over a 10 minute period in two equal phases (sitting followed by standing). A power spectral density analysis was performed for mediolateral sway and BP for each subject. Results: Postural sway power was higher in elderly subjects with distinctive low frequency peaks (<0.1 Hz) that matched peaks found in blood pressure. Both COP and BP data had variation in the higher frequency range (0.15–0.25 Hz) that corresponded to respiration. The observed increase, with age, in postural sway in a region corresponding to low frequency blood pressure variation may represent an increase in skeletal muscle pump activity to maintain blood pressure in the elderly due to a reduction in baroreflex with age. Funded by Simon Fraser University .

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)0.000
metaresearch head score (Gemma)0.000
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 score0.471
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

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