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Record W4409353054 · doi:10.1080/24748668.2025.2490313

Kinematic differences in a skating stop-and-go task between male and female ice hockey players

2025· article· en· W4409353054 on OpenAlexafffund
Shawn M. Robbins, Aiden Hallihan, Philippe J. Renaud, Aïda Valevicius, Laura Holman, Brian D. McPhee, David J. Pearsall

Bibliographic record

VenueInternational Journal of Performance Analysis in Sport · 2025
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIce hockeySpeed skatingKinematicsTask (project management)PsychologyAeronauticsApplied psychologyPhysical medicine and rehabilitationSimulationComputer scienceMedicineEngineeringPhysics

Abstract

fetched live from OpenAlex

Kinematic differences exist between sexes when skating, but other tasks have not been compared. The objective was to compare the centre of mass (COM) and joint angles between female and male ice hockey players during a Stop-and-Go task. Male (n = 8) and female (n = 9) players completed a Stop-and-Go requiring them to skate (PRE phase), complete a side parallel stop (STOP phase), and then skate back to the initial position (POST phase). Kinematic data were collected with an 18-camera motion capture system. Spatiotemporal variables, COM, and joint angles were calculated. T-tests compared spatiotemporal variables between sexes, while 2-way analysis of variance compared COM and joint angle discrete values between sexes and phases. Males completed Stop-and-Go faster than females (mean difference = 0.36 s, p = 0.035). Females had higher COM vertical positions (p = 0.023) and lower velocities in the forward direction (p < 0.001). During PRE phase at the inside hip, females had greater adduction (p = 0.006), less extension (p = 0.027), and less flexion range of motion (p = 0.027). Males had greater bilateral knee flexion (p ≤ 0.029) across the phases. Joint angle and COM differences might account for the faster completion times in males. Increasing joint flexion maybe a strategy to improve performance during changes in direction in ice hockey players.

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 score0.010
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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