A New In-Skates Balance Error Scoring System for Ice Hockey Players
Bibliographic record
Abstract
OBJECTIVE: Evaluate a new in-skates balance error scoring system (SBESS) for ice hockey players wearing their equipment. DESIGN: Prospective, randomized, single blinded study. SETTING: Sport Medicine Clinic. PARTICIPANTS: Eighty university hockey players. INTERVENTION: A control group performed the SBESS assessment at rest on 2 separate occasions and an experimental group performed the assessment at rest and after exertion. The SBESS consists of maintaining different stances on ice skates for 20 seconds each, while wearing full equipment (no stick, gloves and helmet) and standing on a hard rubber surface. Three independent reviewers scored the video recorded assessments. MAIN OUTCOMES MEASURES: Primary outcome was the number of balance errors and the secondary outcome was the number of falls. RESULTS: The control group's median SBESS scores were 2 and 3 on the first and second attempts at rest, respectively. The experimental group's median SBESS scores were 2 at rest and 2 after exertion. There was no fatigue effect and no athletes fell while performing the test. Of the 4 stances tested, the tandem stance had the highest variability in error scores between athletes and when repeated by the same athlete. The intraclass correlation coefficient (ICC) for interrater reliability was above 0.82, and the intrarater reliability ICC was above 0.86 for all SBESS scores. There was no concordance between the SBESS and the modified BESS. CONCLUSIONS: The SBESS, omitting the tandem stance, is a safe and reproducible sideline balance assessment of ice hockey players wearing full equipment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".