Tissue mass ratios and the reporting of distal lower extremity injuries in varsity athletes at a Canadian University
Bibliographic record
Abstract
The purpose of this preliminary investigation was to determine the relative role of the distal lower extremity tissue masses of varsity athletes in predicting distal lower extremity injury sustained during a competitive season. One hundred male and female varsity athletes (basketball, volleyball, soccer, cross country) completed a questionnaire on general health, physiological, and psychosocial variables, during each sport's respective training camp. A series of anthropometric measurements were used as inputs to distal lower extremity tissue mass prediction equations to calculate lean mass, fat mass, bone mineral content and wobbling mass (lean mass + fat mass) and tissue mass ratios. Athletes were monitored throughout their respective seasons and were instructed to report any distal lower extremity injuries to a certified athletic therapist who was responsible for assessing and confirming the reports. Logistic regression analyses were performed to determine which variables significantly predicted distal lower extremity injury. Mean leg fat mass:bone mass (OR = 1.6, CI = 1.0 - 2.5), and competition surface (rubber OR = 8.5, CI = 1.5 - 47.7; artificial turf OR = 4.0, CI = 0.77 - 22.9) were identified as significant predictors of injury. Overall, tibia bone injuries were significantly associated with the ratio of fat mass:bone mineral content and the surface on which the athletes compete.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".