Do children and adolescent ice hockey players with and without a history of concussion differ in robotic testing of sensory, motor and cognitive function?
Bibliographic record
Abstract
BACKGROUND: KINARM end point robotic testing on a range of tasks evaluating sensory, motor and cognitive function in children/adolescents with no neurologic impairment has been shown to be reliable. The objective of this study was to determine whether differences in baseline performance on multiple robotic tasks could be identified between pediatric/adolescent ice hockey players (age range 10-14) with and without a history of concussion. METHODS: Three hundred and eighty-five pediatric/adolescent ice hockey players (ages 10-14) completed robotic testing (94 with and 292 without a history of concussion). Five robotic tasks characterized sensorimotor and/or cognitive performance with assessment of reaching, position sense, bimanual motor function, visuospatial skills, attention and decision-making. Seventy-six performance parameters are reported across all tasks. RESULTS: There were no significant differences in performance demonstrated between children with a history of concussion [median number of days since last concussion: 480 (range 8-3330)] and those without across all five tasks. Performance by the children with no history of concussion was used to identify parameter reference ranges that spanned 95 % of the group. All 76 parameter means from the concussion group fell within the normative reference ranges. CONCLUSIONS: There are no differences in sensorimotor and/or cognitive performance across multiple parameters using KINARM end point robotic testing in children/adolescents with or without a history of concussion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".