The relationship between body composition measured by dual-energy X-ray absorptiometry and maximal strength in classic powerlifting
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to conduct a detailed body composition analysis by Dual-Energy X-Ray Absorptiometry (DEXA scan) on classic powerlifters to understand better the relationship between various body composition variables and maximal strength. METHODS: Fifteen drug-free classic powerlifters (9 males and 6 females) underwent a DEXA scan and absolute (kg) and relative (Wilks) maximal strength from all 3 powerlifting events (squat, bench and deadlift) was registered from the previous provincial championship. A 2 tailed Pearson correlation analysis was conducted in order to present the relationships between body composition variables and performance measures. RESULTS: The main outcome of the present study is that experience in resistance training (r=0.52 to 0.74), lean body tissue (r= 0.61 to 0.95), arms mass (r=0.57 to 0.86), legs mass (r=0.53 to 0.63), bone mineral content (BMC) (r=0.52 to 0.91) and bone mineral density (BMD) (r=0.53 to 91) measures were significantly correlated (P<0.05) with most absolute (kg) and relative (Wilks) maximal strength measures. CONCLUSIONS: The results of the present study could guide practitioners working with athletes aged between 22 and 35 years old practicing sports with higher risks of fractures (e.g., alpine skiing) to potentially utilize the squat, the bench press and the deadlift not only to build strength in order to increase sport performance, but also to increase BMC and BMD of their athletes trough heavy eccentric loading to help reduce the risks of fractures. Further studies should be directed towards interventions trying to reveal that some type of relationship does exist between BMC, BMD and powerlifting performance and in what direction.
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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 | 0.002 |
| 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) | 0.003 | 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".