Seasonal Changes in Functional Fitness and Neurocognitive Assessments in Youth Ice-Hockey Players
Bibliographic record
Abstract
Avery, M, Wattie, N, Holmes, M, and Dogra, S. Seasonal changes in functional fitness and neurocognitive assessments in youth ice-hockey players. J Strength Cond Res 32(11): 3143-3152, 2018-Deficits in balance and strength combined with differing rates of sensory and motor neural development may increase risk of sports-related injury in youth. This study evaluated changes in functional fitness and concussion status over the course of a competitive season in youth ice-hockey players, and examined the relationship between these variables and injury occurrence. Thirty-six participants (8.9 ± 1.1 years) completed preseason and postseason assessments including anthropometric measurements, the functional movement screen (FMS), Lower Quarter Y-Balance Test (YBT-LQ), and a computerized neurocognitive assessment (ImPACT). Paired-samples t-tests were used to compare preseason and postseason data. Independent samples t-tests were used to compare FMS, YBT-LQ, and ImPACT scores between injured and uninjured participants at baseline. The mean composite score of the FMS was not statistically different between preseason (15.1 ± 1.8) and postseason (15.6 ± 2.3, p < 0.16). The YBT-LQ composite score showed a decrease in reach distance scores between preseason (86.10 ± 6.00) and postseason (83.20 ± 5.40, p < 0.001). Neurocognitive assessment scores improved in both the injured and uninjured participants. There were no significant relationships between FMS scores, YBT-LQ, and injury incidence. Results from our study provide insight into changes in functional fitness levels and neurocognitive scores over the course of a season in youth ice-hockey players. These findings provide insight into expected changes over the course of a season and provide context for injury risk monitoring by coaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".