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Record W4399892137 · doi:10.1123/ijspp.2023-0423

Intraday Variation of Ankle Dorsiflexion in Short-Track Speed Skaters

2024· article· en· W4399892137 on OpenAlexaff
Jules Claudel, Émilie Turner, Julien Clément

Bibliographic record

VenueInternational Journal of Sports Physiology and Performance · 2024
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSpeed skatingAnkle dorsiflexionTrack (disk drive)Variation (astronomy)AnklePhysical medicine and rehabilitationComputer scienceSimulationMedicineAnatomy

Abstract

fetched live from OpenAlex

PURPOSE: Optimal ankle dorsiflexion range of motion plays a vital role in attaining the essential crouched posture necessary for excelling in speed skating. The purpose of this study was to determine how the ankle dorsiflexion angle evolves throughout a day of training and to identify the factors that influence this angle. METHODS: Thirty short-track speed skaters, from 2 teams, participated in this study. The maximum ankle dorsiflexion angle was obtained in a lunge position facing a wall, using a digital inclinometer. All measures were obtained 3 times per side, 6 times per day, on 2 training days separated by at least a week. We conducted multiple tests to study the impact of repetition, day, side, team level, sex, and moment on the ankle dorsiflexion angle. RESULTS: The 3 times repeated measures and the 2 days of training did not have a significant influence on the results. There was a statistically significant difference between the first time point of the day and the 5 other time points for both ankles. Moreover, the influence of sex and team level was not statistically significant. CONCLUSIONS: The results indicate that there are significant changes in ankle dorsiflexion range of motion but only after the first warm-up of the day. Such findings could enable team staff to enhance athletes' precompetition preparation and tailor ankle mobility training regimens more effectively.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.233

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)0.0000.000

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 head0.010
GPT teacher head0.289
Teacher spread0.279 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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