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Injury Rates in House League, Select, and Representative Youth Ice Hockey

2005· article· en· W2144497504 on OpenAlexaff
Barry Willer, Beth Kroetsch, Scott R. Darling, Alan D. Hutson, John J. Leddy

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2005
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsJoseph Brant Hospital
Fundersnot available
KeywordsLeagueIce hockeyInjury preventionDemographyMedicinePoison controlPhysical therapyPhysical medicine and rehabilitationMedical emergency

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine injury rates in a youth ice hockey program over two seasons (2002-2004). Injury rates for age groups (4-18 yr) and for different levels of competition were compared. Another purpose was to determine the effect of body checking on injury rates among these youths. METHODS: A prospective injury report form was completed by a volunteer trainer for each injury that caused a loss of player time and resulted in evaluation by a physician. The injury form documented age group, type of injury, length of time that the player missed action due to the injury, location of the injury, and circumstances that led to the injury. Participants included 2632 boys aged 4-18 who played in the 2002-2003 season and 2639 boys who played in the 2003-2004 season. RESULTS: Injuries were four times more likely to occur in games than practices. Boys who played in the most advanced levels of competition are 6.1 times more likely to be injured than boys playing in house leagues. Injury rates during games showed a trend toward increasing with the age of the player. Injury rates spiked the first year that body checking was introduced in two different competition levels. Injury rates also spiked with the onset of adolescence (age 13). CONCLUSION: The study findings suggest that the introduction of body checking at age 9 to competitive youth hockey causes an immediate but relatively short-term increase in injury rates. The period of adjustment that accompanies body checking should be taken into account when determining the age at which body checking is introduced.

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)0.002
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 score0.074
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.324
Teacher spread0.306 · 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

Citations64
Published2005
Admission routes1
Has abstractyes

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