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Record W2041622260 · doi:10.1080/02640414.2012.757343

Child–adult differences in the kinetics of torque development

2013· article· en· W2041622260 on OpenAlexafffund
Raffy Dotan, Cameron J. Mitchell, Rotem Cohen, David A. Gabriel, Panagiota Klentrou, Bareket Falk

Bibliographic record

VenueJournal of Sports Sciences · 2013
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcMaster UniversityBrock University
FundersCanadian Institutes of Health Research
KeywordsIsometric exerciseElbow flexionKineticsDynamometerExplosive strengthElbowMotor unitMathematicsPhysical medicine and rehabilitationMedicinePhysical therapyAnatomyPhysics

Abstract

fetched live from OpenAlex

Children have lower size-normalised maximal voluntary force, speed, and power than adults. It has been hypothesised that these and other age-related performance differences are due to lesser type-II motor-unit utilisation in children. This should be manifested as slower force kinetics in explosive muscle contractions. The purpose of this study was to investigate the nature of child-adult force-kinetics differences and whether the latter could support that hypothesis. Untrained boys (n = 20) and men (n = 20) (10.1 ± 1.3 and 22.9 ± 4.4 years, respectively), performed maximal, explosive, isometric elbow flexions and knee extensions on a Biodex dynamometer. Peak torque (MVC), times to 10-100% MVC, and other kinetics parameters were determined. The boys' body-mass-normalised knee extension MVC, peak rate of torque development, and %MVC at 100 ms were 26, 17 and 23% lower compared with the men and their times to 30% and 80% MVC were 24 and 48% longer, respectively. Elbow flexion kinetics showed similar or greater differences. The findings illuminate boys' inherent disadvantage in tasks requiring speed or explosive force. It is demonstrated that the extent of the boys-men kinetics disparity cannot be explained by muscle-composition and/or musculo-tendinous-stiffness differences. We suggest therefore that the findings indirectly support children's lower utilisation of type-II motor units.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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 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

Citations44
Published2013
Admission routes2
Has abstractyes

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