Bibliographic record
Abstract
Injury and participation information was collected over 5 years (1993-1997) on varsity men's football players in the Canada West Universities Athletic Association. The locations of acute time-loss injuries or neurologic injures were coded as head and neck, upper extremity (shoulder to hand), or lower extremity (hip to foot). Poisson regression-based generalized estimating equations were used to estimate rate ratios and 95% confidence intervals. Injury rates were higher during games as compared with practice periods (for the head and neck, rate ratio (RR) = 9.75 (95% confidence interval (CI): 7.50, 12.67); for upper extremities, RR = 5.76 (95% CI: 4.46, 7.45); and for lower extremities, RR = 7.06 (95% CI: 6.03, 8.25)). In dry-field game situations, head and neck injury rates were 1.59 times higher on artificial turf than on natural grass (95% CI: 1.04, 2.42). Lower extremity game injury rates were higher on artificial turf than on natural grass under both dry (RR = 1.83, 95% CI: 1.35, 2.48) and wet (RR = 2.31, 95% CI: 1.18, 4.52) field conditions. Injury rates increased with every additional year of participation. Past injury increased the rate of subsequent injury. The effect of an artificial field surface may be related to infrequent use. Risk factors for injury included participation in a game, playing on artificial turf, being a veteran player, and having a past injury.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".