MétaCan
Menu
Back to cohort
Record W4402821766 · doi:10.1002/dta.3804

Skeletal Muscle Memory: An Update From the Antidoping Perspective

2024· review· en· W4402821766 on OpenAlexaff
Claire Traversa

Bibliographic record

VenueDrug Testing and Analysis · 2024
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsMcGill UniversityWorld Anti-Doping Agency
Fundersnot available
KeywordsMuscle hypertrophyContext (archaeology)AnabolismSkeletal muscleEpigeneticsDNA methylationAthletesNeuroscienceBiologyPsychologyMedicineEndocrinologyGeneticsGene expressionPhysical therapyGene

Abstract

fetched live from OpenAlex

This narrative review explores the concept of muscle memory, focusing on the physiological and biochemical mechanisms underlying information retention in skeletal muscle tissue as it relates to antidoping. The discussion encompasses the role of satellite cells (SCs) in myonuclei recruitment, resulting in increased myonuclear density and heightened muscle protein turnover. The myonuclear domain theory suggests that myonuclei acquired during hypertrophy may persist, contributing to enhanced muscle protein synthesis (MPS) and potential benefits of muscle memory. The impact of sustained training, protein intake, and resistance exercise on muscle memory, especially in elite athletes, is considered. The review also delves into the influence of anabolic androgenic steroids (AAS) on muscle tissue, highlighting their role in elevating the performance threshold and supporting recovery during intense training through increased muscle protein turnover rates. Additionally, genetic and epigenetic modifications, such as DNA methylation, are explored as potential contributors to muscle memory. The complex interplay of continuous training, AAS use, and genetic factors offers avenues for further research, especially in the context of antidoping efforts. The understanding of muscle memory has implications for maintaining performance gains and addressing ethical challenges in sports.

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)0.001
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 score0.985
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
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 head0.058
GPT teacher head0.349
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDrug Testing and AnalysisSame topicHormonal and reproductive studiesFrench-language works237,207