Musculoskeletal Injury in the Masters Runners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it