Navigating the Ice: Establishing Pre-Concussion Baselines in Ice-Hockey Players for Gait Assessment Utilizing Inertial Measurement Units
Bibliographic record
Abstract
Introduction Ice hockey can lead to high-energy collisions and traumas and is a sport with high risk of concussion (Ornon et al., 2020). Concussions represent 2–14% of all hockey injuries (Izraelski, 2014). Sports-related concussions (SRC) sustained in professional ice hockey are a common in-competition injury leading to highly individual return to sport that can be associated with symptoms lasting days to months (Höllerer et al., 2023). Following SRC there is an increased risk of subsequent concussion and musculoskeletal injury upon return to play, however, assessments that can detect subclinical changes in function following a concussion are lacking (Dunne et al., 2023). A COSMIN systematic review suggests gait-based assessments using inertial measurement units to capture pre-concussion baseline scores (Dunne et al., 2023). This study measured pre-season pre-concussion gait data in Swiss National League Ice hockey players. Baseline scores will be compared with measures from players sustaining in-season SRC. Methods Temporal and spatial gait parameters were measured with the Physilog (https://www.physilog.com/ , Lausanne, Switzerland) movement sensors. The sensors were fixed to the shoes for flat overground gait analysis. Over a 20-30 m walkway, participants performed a single-task (ST) walking condition (preferred gait speed) and a dual-task (DT) walking condition, i.e., preferred gait speed whilst counting backwards. Application of the dual-task paradigm aimed at quantifying the automaticity of movement (Soulard et al., 2021). The focus of this study was on gait speed, cadence (+ cv%), and stride length (+ cv%) (Dunne et al., 2023). Results Thirteen male National League players from SC Rapperswil-Jona Lakers, 21.9 ± 3.1 years, 182.5 ± 5.9 cm height, 83.3 ± 7.6 kg body weight performed a pre-season gait analysis. The players sustained 1.5 ± 1.4 [range 0 – 3] concussions in the past. ST/DT walking revealed a walking speed of 1.3 ± 0.14/1.12 ± 0.14 m/s; step length 0.72 ± 0.09/0.67 ± 0.07 m; step length coefficient of variation (cv, %) 5.4 ± 1.9/5.6 ± 1.4 %; cadence 106.7 ± 5.1/100.5 ± 5.7 steps/min; cadence cv 2.8 ± 1/2.9 ± 0.9 %. Discussion/Conclusion Our results will shed light on the reliability and validity of using inertial measurements in the context of concussion management. Practitioners can use this resource at their disposal to help make informed decisions regarding concussion management. References Dunne, L. A. M., et al. (2023). Validity and reliability of methods to assess movement deficiencies following concussion: A COSMIN systematic review. Sports Medicine - Open, 9, 76. https://doi.org/10.1186/s40798-023-00625-0 Höllerer, D., et al. (2023). Injury incidence, outcomes, and return to competition times after sports-related concussions during one professional ice hockey season: A prospective cohort study. Healthcare, 11, 3153. https://doi.org/10.3390/healthcare11243153 Izraelski, J. (2014). Concussions in the NHL: A narrative review of the literature. Journal of the Canadian Chiropractic Association, 58(4), 346–352. Ornon, G., et al. (2020). Epidemiology of injuries in professional ice hockey: A prospective study over seven years. Journal of Experimental Orthopaedics, 7, 87. https://doi.org/10.1186/s40634-020-00300-3 Soulard, J., et al. (2021). Spatio-temporal gait parameters obtained from foot-worn inertial sensors are reliable in healthy adults in single- and dual-task conditions. Scientific Reports, 11, 10229. https://doi.org/10.1038/s41598-021-88794-4
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".