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Record W2920403325 · doi:10.1002/mus.25637

Cross‐education after high‐frequency versus low‐frequency volume‐matched handgrip training

2017· article· en· W2920403325 on OpenAlexaff
Natasha G. Boyes, Peter Yee, Joel L. Lanovaz, Jonathan P. Farthing

Bibliographic record

VenueMuscle & Nerve · 2017
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIsometric exerciseMedicineWristElectromyographyPhysical medicine and rehabilitationPhysical therapyHand strengthMuscle hypertrophyInternal medicineGrip strengthAnatomy

Abstract

fetched live from OpenAlex

INTRODUCTION: Cross-education training programs cause interlimb asymmetry of strength and hypertrophy. We examined the cross-education effects from a high-frequency (HF) versus a low-frequency (LF) volume-matched handgrip training program on interlimb asymmetry. METHODS: Right-handed participants completed either HF (n = 10; 2 × 6 repetitions 10 times per week) or LF (n = 9; 5 × 8 repetitions 3 times per week) training. Testing occurred twice before and once after 4 weeks of right-handed isometric handgrip training totaling 120 weekly repetitions. Measures were maximal isometric handgrip and wrist flexion torque, muscle thickness, and muscle activation (electromyography; EMG). RESULTS: Grip strength was greater in both limbs posttraining, pooled across groups (P < 0.001). Trained limb muscle thickness increased in both groups (P < 0.05; untrained, P = 0.897). EMG and wrist flexion torque did not change (all P > 0.103). DISCUSSION: Both LF and HF induced cross-education of grip strength to the untrained limb, but HF did not reduce asymmetry. These findings have implications for injury rehabilitation. Muscle Nerve 56: 689-695, 2017.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.030
GPT teacher head0.321
Teacher spread0.292 · 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.

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

Citations18
Published2017
Admission routes1
Has abstractyes

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