The Role of the Hand Surgery Consultant in the Care of the Hockey Athlete
Bibliographic record
Abstract
Ice hockey is a high-speed sport played on a slippery ice surface, using sharp skate blades, rigid sticks with a hard rubber puck, and allowing full-force physical contact that includes body checking and fighting. Although many of the same injuries to the hand and wrist occur that are seen in other sports, there is a higher frequency of certain injuries in the hockey athlete due to the forces involved, the way the hockey stick is gripped, torqued, and used, and the fact that players can slash one another with their sticks. Beyond timely and accurate management of the injury itself, the role of the consultant hand surgeon in hockey encompasses mastery of the intangible skills in the art and humanity of medical care. Injury to the hockey athlete sets in motion a dynamic process that involves many stakeholders who each require your knowledge of how this will affect the hockey athlete's immediate and long-term health, how long they may be out of competition, and the kind of treatments, splints and equipment modifications that could help foster the earliest return-to-play in a safe manner. The consultant hand surgeon needs the ability to communicate information at a high level to team physicians and trainers while remaining nimble enough to simplify that information for general managers, coaches, agents, and athletes. The role demands commitment in time and flexibility, remaining open to gray areas in treatment options, possessing a creative mindset for problem-solving, and the ability to quickly assimilate vast amounts of information to provide a risk assessment of short and long-term implications the injury presents to both the player and the team.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".