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Record W1572601915 · doi:10.1159/000085393

Ice Hockey Injuries

2005· review· en· W1572601915 on OpenAlexaff
Brian W. Benson, Willem Meeuwisse

Bibliographic record

VenueMedicine and sport science/Medicine and sport · 2005
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIce hockeyAeronauticsPsychologyPhysical medicine and rehabilitationMedicineEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: This article reviews the distribution and determinants of injuries reported in the pediatric ice hockey literature, and suggests potential injury prevention strategies and directions for further research. DATA SOURCES: Thirteen electronic databases, the ISI Web of Science, and 'grey literature' databases were searched using a combination of Medical Subject Headings and text words to identify potentially relevant articles. The bibliographies of selected studies were searched to identify additional articles. Studies were selected for review based on predetermined inclusion and exclusion criteria. MAIN RESULTS: A comparison between studies on this topic area was difficult due to the variability in research designs, definition of injury, study populations, and measurements used to assess injury. The majority of injuries were sustained during games compared with practices. The two most commonly reported injuries were sprains/strains and contusions. Players competing at the Minor hockey, High School, and Junior levels of competition sustained most of their injuries to the upper extremity, head, and lower extremity, respectively. The primary mechanism of injury was body checking, followed by stick and puck contact. The frequency of catastrophic eye injuries has been significantly reduced with the world-wide mandation of full facial protection for all Minor hockey players. CONCLUSIONS: Specific hockey-related injury risk factors are poorly delineated and rarely studied among pediatric ice hockey players leaving large gaps in the knowledge of appropriate prevention strategies. Risk management strategies should be focused at avoiding unnecessary foreseeable risk, and controlling the risks inherent to the sport. Suggestions for injury prevention and future research are discussed.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0020.003
Science and technology studies0.0010.010
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.152
GPT teacher head0.443
Teacher spread0.291 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations62
Published2005
Admission routes1
Has abstractyes

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