A retrospective case-control analysis of 2002 running injuries
Bibliographic record
Abstract
OBJECTIVE: To provide an extensive and up to date database for specific running related injuries, across the sexes, as seen at a primary care sports medicine facility, and to assess the relative risk for individual injuries based on investigation of selected risk factors. METHODS: Patient data were recorded by doctors at the Allan McGavin Sports Medicine Centre over a two year period. They included assessment of anthropometric, training, and biomechanical information. A model was constructed (with odds ratios and their 95% confidence intervals) of possible contributing factors using a dependent variable of runners with a specific injury and comparing them with a control group of runners who experienced a different injury. Variables included in the model were: height, weight, body mass index, age, activity history, weekly activity, history of injury, and calibre of runner. RESULTS: Most of the study group were women (54%). Some injuries occurred with a significantly higher frequency in one sex. Being less than 34 years old was reported as a risk factor across the sexes for patellofemoral pain syndrome, and in men for iliotibial band friction syndrome, patellar tendinopathy, and tibial stress syndrome. Being active for less than 8.5 years was positively associated with injury in both sexes for tibial stress syndrome; and women with a body mass index less than 21 kg/m(2) were at a significantly higher risk for tibial stress fractures and spinal injuries. Patellofemoral pain syndrome was the most common injury, followed by iliotibial band friction syndrome, plantar fasciitis, meniscal injuries of the knee, and tibial stress syndrome. CONCLUSIONS: Although various risk factors were shown to be positively associated with a risk for, or protection from, specific injuries, future research should include a non-injured control group and a more precise measure of weekly running distance and running experience to validate these results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".