MUSCULOSKELETAL INJURY IN THE MASTERS RUNNER
Bibliographic record
Abstract
The aging and health conscious population drive the need to study the masters athlete. Musculoskeletal changes that occur with age may predispose this age group to different types of injuries than a younger population. Shoes and orthotics designed specifically for the masters athlete may be warranted to prevent these injuries and keep the aging population active. PURPOSE To identify age related differences in running injury patterns. METHODS Retrospective survey data was collected on participants in the Hood to Coast running relay race (Oregon, USA). Surveys were distributed via email at 1 and 3 weeks pre-race and 1 week post-race. Hard copies were made available to all athletes (N=12300) at the race site. Athletes completed the survey only once and reported injury frequency, location, diagnosis, and training variables for the previous year. Chi-square analysis was used to determine differences by age. RESULTS Preliminary results include data from 2669 runners. Eighty-two percent reported running 3–5 times/week and 69% reported running 11–30 miles/week. The injury rate was consistent across age groups at 46% (SD=1.73%). The knee comprised 25% of all injuries and was the most common location in all age groups (SD=6.9%). Injuries to the foot and lower leg followed in those under age 40, however hamstring and achilles injuries superseded lower leg injuries in runners over age Only 56% of injured runners sought advice from a physician or other health professional for injury diagnosis. Orthotic use was greater in those over age 40. CONCLUSION Injury rates are consistent across age groups. Patterns of injury location change with age. These results have important implications for operative and non-operative treatment plans and for footwear and orthotic design to prevent injuries and keep our aging population active. Supported by the Nike Sports Research Laboratory, Beaverton, Oregon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".