Differences in distal lower extremity tissue masses and mass ratios exist in athletes of sports involving repetitive impacts
Bibliographic record
Abstract
This study aimed to examine the effects of sex and sport on the tissue composition of the distal lower extremity of varsity athletes, in sports that involve repetitive-impact loading patterns. Fat mass, lean mass, bone mineral content and wobbling mass were predicted for the leg and leg + foot segments of varsity basketball, cross-country, soccer and volleyball athletes. The absolute masses were normalised to body mass, and also expressed relative to each other as ratios. Females and males differed on most normalised tissue masses and ratios by 11-101%. Characteristic differences were found in the normalised tissue masses across sports, with the lowest and highest values displayed by cross-country and volleyball (female)/basketball (male) athletes, respectively. Conversely, cross-country athletes had the highest wobbling mass:bone mineral content and lean mass:bone mineral content ratios for females by 10% and 16%, respectively. The differences between sports may be explained in part by different impact loading patterns characteristic of each sport. Tissue mass ratio differences between sports may suggest that the ratios of soft to rigid tissues are optimised by the body in response to typical loading patterns, and may therefore be useful in investigations of distal lower extremity injury mechanisms in athletes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".