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Record W2097709598 · doi:10.1007/s12160-014-9639-2

Sex Differences in the Prediction of Metabolic Burden from Physiological Responses to Stress

2014· article· en· W2097709598 on OpenAlexafffund
Christina Gentile, Anda I. Dragomir, Crina Solomon, Anil Nigam, Bianca D’Antono

Bibliographic record

VenueAnnals of Behavioral Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersCanadian Institutes of Health Research
KeywordsStressorHeart rateBlood pressureMetabolic syndromeQuartileMedicineHeart rate variabilityPhysiologySex characteristicsInternal medicineObesityClinical psychologyConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Heightened or prolonged physiological responses to stress may contribute to the development or progression of metabolic abnormalities. PURPOSE: This study aims to examine the prospective relationships between stress responses and metabolic burden, and to determine whether age and/or sex moderate these relationships. METHODS: One hundred ninety-nine healthy men and women (M(age) = 41 ± 11.5) were exposed to four stressors while blood pressure, heart rate, and heart rate variability were obtained. Residual change scores for reactivity (stress - baseline) and recovery (post-stress - baseline) scores were computed. Metabolic burden refers to the number of metabolic parameters for which participants were in the highest quartile (lowest for high-density lipoprotein cholesterol) for their sex. Metabolic burden was reassessed in 136 participants 3 years later. RESULTS: Greater parasympathetic withdrawal in response to stress was associated with increased metabolic burden, though this was evident mostly in men. In women, dampened autonomic responses to stress were associated with higher metabolic burden. CONCLUSIONS: Cardiac autonomic responses to stress predict future metabolic abnormalities, though the direction of effect differs according to sex.

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)0.000
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 score0.624
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.164
GPT teacher head0.378
Teacher spread0.214 · 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

Citations12
Published2014
Admission routes2
Has abstractyes

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