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Record W4402595499 · doi:10.2519/josptopen.2024.0069

Associations Between Early Specialization and Ice Hockey Goaltender Hip Kinematics: A Cross-sectional Study

2024· article· en· W4402595499 on OpenAlexaff
Margaret S. Harrington, Courtney A. Hlady, Timothy A. Burkhart

Bibliographic record

VenueJOSPT open. · 2024
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIce hockeyKinematicsCross-sectional studyPhysical medicine and rehabilitationMedicinePhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare hip kinematics between early specialized (ES) and not early specialized (NES) ice hockey goaltenders. DESIGN: Cross-sectional study. METHODS: Twenty-six goaltenders’ (13 ES) kinematics were quantified during common goaltending tasks (ie, butterfly drops, power and butterfly slides) performed on a slide board using Theia3D markerless technology. Maximum and minimum hip flexion, adduction, and internal rotation (IR) angles were determined, as were the concurrent hip angles in the two other planes at these positions. The groups were compared using independent t tests or Mann-Whitney U tests for discrete data and statistical parametric mapping for hip angles over time. RESULTS: ES goaltenders had increased IR and abduction at lower flexion and less IR and abduction at higher flexion compared to NES goaltenders. Neither group reached the expected extreme ranges of flexion, adduction, or IR typically associated with mechanical bony impingement of femoroacetabular impingement syndrome (FAIS). CONCLUSION: The ES goaltenders may minimize combined flexion and IR or abduction to avoid pain in hips due to FAIS or labral tears or have adopted advantageous hip control strategies to avoid abnormal hip contact mechanics that contribute to developing these pathologies. JOSPT Open 2025;3(1):35-44. Epub 18 September 2024. doi:10.2519/josptopen.2024.0069

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 score0.003
Threshold uncertainty score0.466

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 head0.062
GPT teacher head0.388
Teacher spread0.326 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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