A prospective study of running injuries: the Vancouver Sun Run “In Training” clinics
Bibliographic record
Abstract
OBJECTIVES: Seventeen running training clinics were investigated to determine the number of injuries that occur in a running programme designed to minimise the injury rate for athletes training for a 10 km race. The relative contributions of factors associated with injury were also reported. METHODS: A total of 844 primarily recreational runners were surveyed in three trials on the 4th, 8th, and 12th week of the 13 week programme of the "In Training" running clinics. Participants were classified as injured if they experienced at least a grade 1 injury-that is, pain only after running. Logistic regression modelling and odds ratio calculation were performed for each sex using the following predictor variables: age, body mass index (BMI), previous aerobic activity, running frequency, predominant running surface, arch height, running shoe age, and concurrent cross training. RESULTS: Age played an important part in injury in women: being over 50 years old was a risk factor for overall injury, and being less than 31 years was protective against new injury. Running only one day a week showed a non-significant trend for injury risk in men and was a significant risk factor in women and overall injury. A BMI of > 26 kg/m(2) was reported as protective for men. Running shoe age also significantly contributed to the injury model. Half of the participants who reported an injury had had a previous injury; 42% of these reported that they were not completely rehabilitated on starting the 13 week training programme. An injury rate of 29.5% was recorded across all training clinics surveyed. The knee was the most commonly injured site. CONCLUSIONS: Although age, BMI, running frequency (days a week), and running shoe age were associated with injury, these results do not take into account an adequate measure of exposure time to injury, running experience, or previous injury and should thus be viewed accordingly. In addition, the reason for the discrepancy in injury rate between these 17 clinics requires further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".