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Record W2015684036 · doi:10.1080/10284150600903552

Activity of autonomic nervous system is related to body weight in pre-menopausal, but not in post-menopausal women

2006· article· en· W2015684036 on OpenAlexfundno aff
Marcellino Monda, Giovanni Messina, C. Vicidomini, A. Viggiano, C Mangoni, Bruno De Luca

Bibliographic record

VenueNutritional Neuroscience · 2006
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsMenopauseHeart rate variabilityMedicinePost menopausalInternal medicineAutonomic nervous systemObesityEndocrinologyLean body massHeart rateBody weightBlood pressure

Abstract

fetched live from OpenAlex

This study analyzed vegetative modulation, expressed as heart rate variability (HRV) power spectral analysis, in lean and obese women at pre-menopausal or post-menopausal age to reveal possible differences in menopause-related autonomic activity in lean and obese subjects. Sedentary women (n = 40) were divided in four groups: pre-menopausal lean and obese women, post-menopausal lean and obese subjects. The HRV-power spectrum was evaluated on a 5-min long ECG recording. The absolute values of the spectrum were summed in the following frequencies: a low-frequency (0.04-0.15 Hz; LF) and high-frequency (0.15-0.40; HF) range. LF and HF were values used to estimate the sympathetic and parasympathetic activity. LF and HF values of pre-menopausal obese women are lower than values of lean women. The menopause induced a same decrease in LF and HF values in lean and obese subjects, so that no difference was found in post-menopausal groups. This experiment indicates that modifications of autonomic modulation can be included among factors related to obesity in pre-menopausal, but not post-menopausal women.

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)0.001
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
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.246
Teacher spread0.238 · 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

Citations42
Published2006
Admission routes1
Has abstractyes

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