Musculoskeletal Injury in the Masters Runners
Bibliographic record
Abstract
OBJECTIVE: To determine if injury patterns and risk factors for injury differ between masters and younger runners. DESIGN: Retrospective survey. SETTING: Hood to Coast running relay, Oregon, USA. PARTICIPANTS: A total of 2886 runners consented to participate and completed the survey. Ninety-four (2712/2886) percent completed the survey electronically and 6% (174/2886) manually. Master runners (>or=40 years) made up 34% of the population. INTERVENTION: The survey was distributed to all participants in the largest running relay in North America. Runners reported on training patterns, injury location, and diagnosis over the previous year. MAIN OUTCOME MEASURES: Descriptive statistics and chi analysis were used to detect differences in injury rate and location between masters and younger runners. Multivariate logistic regression models were used to identify risk factors for injury for each group. RESULTS: The injury rate for the entire population was 46%. Significantly more masters runners were injured than younger runners (P<0.05). More masters runners suffered multiple injuries than younger runners (P<0.001). Significantly more masters runners were male, had 7 or more years of running experience, run more than 30 miles/wk, 6 or more times/week and wear orthotics than younger runners (P<0.001). The knee and foot were the most common locations of injury for both groups. The prevalence of soft-tissue-type injuries to the calf, achilles, and hamstrings was greater in masters runners than their younger counterparts (P<0.001). Younger runners suffered more knee and leg injuries than masters runners (P<0.005). Running more times/wk increased the risk of injury for both groups. CONCLUSIONS: There were subtle differences in injury rate and location between masters runners and younger runners, which may reflect differences in training intensity.
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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 | 0.002 |
| 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) | 0.004 | 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".