Differences in Trunk Strength Between Weightlifters and Wrestlers
Bibliographic record
Abstract
Investigations of trunk strength with high-level athletes are limited. The purpose of this study was to compare maximal concentric isokinetic trunk extension and flexion torque, power, and strength ratios between high-level weightlifters (n = 20), wrestlers (n = 20) and a control (n = 25) population. Isokinetic dynamometry was used to evaluate peak torque, power and strength ratios during seated trunk extension/flexion actions at 60°/s and 180°/s. There were no significant anthropometric differences between groups. Overall, trunk isokinetic force variables as a function of the increase in angular velocity, showed a decrease in peak torque, but an increase in power (athletes and controls). Compared to the control group, athletes demonstrated significantly higher trunk extension torque (+67.05 N·m, ES = 0.81) and power (+49.28 N·m, ES = 0.82) at 60°/s and 180°/s, respectively. Athletes produced significantly greater trunk flexion-extension ratios at 60°/s and 180°/s (ES = 0.80-0.47) than controls. Weightlifters and wrestlers exhibited significantly higher extensor than flexor torque at all angular velocities. Weightlifters demonstrated greater torque (ES = 0.79) than wrestlers at 60°/s. The wrestlers' average power was significantly higher (ES = 0.43) than weightlifters at 180°/s. There were no significant ratio differences between wrestlers (66.23%) and weightlifters (72.06%). Weightlifters had stronger extensor muscles at 60°/s, whereas wrestlers had higher power at 180°/s for extensor muscles. It was postulated that the extensor muscles were stronger than the flexors to ensure trunk stabilisation, and for prevention of injuries. These differences seem to be associated to the movements that occur in each sport in terms of both muscle actions and contractile forces.
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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".