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Record W4401906029 · doi:10.1249/jes.0000000000000346

Hormones, Hypertrophy, and Hype: An Evidence-Guided Primer on Endogenous Endocrine Influences on Exercise-Induced Muscle Hypertrophy

2024· review· en· W4401906029 on OpenAlexafffund
Derrick W. Van Every, Alysha C. D’Souza, Stuart M. Phillips

Bibliographic record

VenueExercise and Sport Sciences Reviews · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
FundersNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Centre of InnovationDairy Farmers of Canada
KeywordsMuscle hypertrophyEndogenyHormoneEndocrinologyEndocrine systemInternal medicineMedicine

Abstract

fetched live from OpenAlex

We review the evidence indicating that endogenous changes in these hormones, including testosterone, growth hormone, insulin growth factor-1, and estrogen, and their proposed anabolic effects contribute to and augment resistance exercise training (RET)-induced hypertrophy. Additionally, we provide recommendations for gold-standard methodological rigor to establish best practices for verifying menstrual phases as part of their research, ultimately enhancing our understanding of the impact of ovarian hormones on RET-induced adaptations.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.137
GPT teacher head0.364
Teacher spread0.228 · 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 designNot applicable
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

Citations31
Published2024
Admission routes2
Has abstractyes

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