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Record W2093264092 · doi:10.1139/h04-007

Anabolic Hormones in Aging Women: Effects of Supplementation vs. Physical Activity

2004· review· en· W2093264092 on OpenAlexaff
Jennifer L. Copeland

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2004
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAnabolismHormoneMedicineEndocrine systemInternal medicineEndocrinologyMuscle atrophyAtrophyPhysical exerciseSarcopeniaPhysiology

Abstract

fetched live from OpenAlex

Aging is associated with a decline in bone mass, muscle mass, strength, and physical function, and women are more likely to suffer from these physical changes than men. The model presented in this paper illustrates the age related changes in anabolic hormones and how this may partly explain the diminished physical function of older women. The model can also be used to identify potential sites of intervention that could delay the atrophy of the musculoskeletal system. Various pharmacological hormone therapies have been shown to be beneficial, but there may be health risks associated with their use. There is evidence that regular physical activity is related to higher levels of anabolic hormones in older persons, therefore exercise could be an alternative to drugs for slowing the age related changes in the endocrine system. However, some research suggests that the hormone response to exercise is blunted in older women. This lower hormonal response may not be a consequence of aging per se but instead may result from secondary characteristics of aging such as a decline in physical fitness and exercise intensity or changes in body composition. Further research is needed to determine whether exercise-induced increases in endogenous hormones have clinical significance in improving muscle or bone mass in aging 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 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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 designOther design
Domainnot available
GenreReview

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

Citations9
Published2004
Admission routes1
Has abstractyes

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