Bibliographic record
Abstract
Introduction: Participation in high impact sports during growth has shown toincrease bone quality1. Gymnastics is one the of the highest impact sports involvingimpacts of up to 10 times body weight2. Gymnasts have previously displayed increasedbone mass and strength; however, it is not yet understood how bone structure isaffected by gymnastics participation. The purpose of this study was to investigate theroll of gymnastics participation on bone structure and bone strength.Methods: The study recruited 17 highly competitive female gymnasts and 15recreationally active controls (16 to 30 years). Areal bone mineral density (aBMD) andbody composition of the hip, spine, and whole body were determined by dual energyx-ray absorptiometry (DXA, Discovery A, Hologic). Bone structure properties weredetermined for the non-dominant tibia and radius using high resolution peripheralquantitative computed tomography (HR-pQCT, Scanco Medical, Brutisellen, Switzerland).Finite element analysis (FEA) was used to determine an in vivo estimation ofbone strength. All data are presented as means standard deviation. Differencesbetween groups were identified with analysis of covariance, adjusting for differencesin body size.Results: Gymnasts had a higher whole body aBMD of 1.0700.122 g/cm2 comparedto controls, 0.9440.123 g/cm2 (p<0.01). Gymnasts also had a larger cross-sectionalarea of their radius, 30539 mm2 versus 24740 mm2 (p<0.01), and tibia, 68365 mm2versus 62666 mm2, than controls. Therefore, FEA revealed gymnasts had strongerbones reflected by a higher failure load, 2830500 N versus 1915503 N (p<0.01) inthe radius, and 7300845 N versus 5810852 N (p<0.01) in the tibia, compared withcontrols.Conclusion: Gymnasts exhibited enhanced bone structure and strength whencompared to inactive controls. The high loading associated with the sport is likely toinduce these desired bone properties.
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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) | 0.004 | 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".