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Record W4392350389 · doi:10.1136/bjsports-2024-ioc.215

660 EP100 – Identifying residual gaps in explosive strength with isokinetic dynamometry

2024· article· en· W4392350389 on OpenAlexaffabout
Félix Croteau

Bibliographic record

VenueE-Posters · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcGill University
Fundersnot available
KeywordsAthletesPhysical therapyWorkloadPhysical medicine and rehabilitationMedicineRank correlationSpearman's rank correlation coefficientMathematicsStatisticsComputer science

Abstract

fetched live from OpenAlex

Background Optimal strength plays a pivotal role in reducing the risk of sports-related injuries; nonetheless, the unique characteristics of strength warrant specific and focused assessment. Objective to evaluate the association between peak torque and the rate of force development (RFD) in a cohort of elite level athletes Design cross-sectional study Setting data obtained from athletes training at Institut National du Sport du Québec Participants 183 healthy athletes partaking in national or international level sport competitions (73 males and 104 females) Assessment of Risk Factors evaluation of torque strength using a Con-TREX MJ system at 60°/s for knee joint muscles and 180°/s for shoulder joint muscles Main Outcome Measurements Spearman rank correlations were employed to compare average peak torque and rates of force development in knee flexion and extension for lower-body sports, as well as shoulder internal and external rotation for upper-body sports. Furthermore, potential sex differences were assessed using Student t-tests. Results Correlation analyses showed high to very high correlation between mean peak torque and RFD for the knee (rho=0.89 to 0.97) and for the shoulder (rho=0.83 to 0.99). Overall, the male athletes exhibited greater values of shoulder torque and RFD than the female athletes (p<0.01). Males were stronger in knee flexion as well, but group differences were not significant for knee extension torque (p=0.08) or RFD (p=0.29). Further analysis of the residuals indicate that outliers strayed further from the mean below the group average (IQR>1.5). Conclusions Mean peak torque and RFD were highly correlated in this cohort of athletes. However, sizeable outliers were present when below-average RFD was present for athletes with typical peak torque. Further research is necessary to explore whether these characteristics are observed in injured athletes, and whether insufficient RFD values may be a better indicator of readiness to return to sport than peak torque.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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