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Record W2953110752

Head, Neck, and Shoulder Injuries in Ice Hockey: Current Concepts.

2018· article· en· W2953110752 on OpenAlexaboutno aff
Charles A. Popkin, Bradley J. Nelson, Caroline N. Park, Steven E. Brooks, T. Sean Lynch, William N. Levine, Christopher S. Ahmad

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsIce hockeyMedicineHead and neckPhysical therapyInjury preventionOccupational safety and healthSuicide preventionPoison controlHuman factors and ergonomicsPhysical medicine and rehabilitationMedical emergencySurgery
DOInot available

Abstract

fetched live from OpenAlex

Ice hockey is a fast-paced contact sport that is becoming increasingly popular in North America. More than 1 million men, women, and juniors are playing hockey in the United States and Canada. With players colliding forcefully with one another and with the boards surrounding the ice, injury rates are among the highest in all of competitive sports. Physicians caring for a hockey team should be aware of the more common injuries, involving the head, the neck, and the shoulder. In this review, we discuss evaluation and treatment of these hockey injuries, return to play, and, where applicable, prevention strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.385
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2018
Admission routes1
Has abstractyes

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