Identifying Risk Factors For Running-Related Injuries And Their Interrelation Using Machine Learning- A Prospective Cohort Study (Smart Injury Prevention)
Bibliographic record
Abstract
PURPOSE: While individual risk factors for running-related injuries (RRI) have been previously identified, the interrelationship of risk factors has not been adequately explored. The aim of this study is to evaluate how machine learning (ML) may identify the combination of risk factors of RRI. METHODS: In this prospective cohort study over 12 months, competitive injury-free adult runners (running at least 20 km per week) were included. Baseline assessment contained medical examination, questionnaires (demographics, sport and injury history, menstruation, medication), musculoskeletal blood laboratory tests as well as biomechanical evaluation via motion capture laboratory (Qualisys AB, Sweden and Theia Markerless, Canada). For the following 12 months, injury surveillance was conducted via the Oslo Sports Trauma Research Center questionnaire (OSTRC-H2 on athletemonitoring.com; FITSTATS Technologies, Inc., Canada). Running exposure was assessed via GPS data (strava.com; Strava, Inc., USA). Any reported injury was followed up and precisely diagnosed by a medical assessment with possible imaging in a specialized running clinic. The main outcome was biomechanical, training-related, and medical baseline risk factors attributing to RRI represented by the injury incidence (injuries per 1000 h running). A ML model will be used to learn the interrelation and ranking of possible risk factors with a sensitivity analysis. RESULTS: Overall, 120 runners (33.3% female, 40.4 ± 10.1 years, 176.4 ± 7.9 cm, 70.6 ± 9.5 kg, BMI 22.6 ± 10.1 kg/m2) were recruited and 114 (95%) reported to the injury monitoring during 11,827 hours of running. Fifty injured runners (prevalence 43.9%, 95%CI 31.7 to 56.0%), reported 94 new injuries corresponding to an incidence rate of 8.0 injuries per 1000 h of running (95%CI 5.7 to 10.2). Of these injuries, most were Achilles tendinopathies (n = 13) and iliotibial band syndromes (n = 10). As of writing the abstract, the ML analysis is still to be conducted. CONCLUSIONS: This prospective cohort study used an extensive baseline assessment with state-of-the-art injury surveillance. A machine learning approach will be used to determine the interrelation of risk factors for RRI which may be a possible base for novel injury risk reduction strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".