Weight Category-dependent Trunk Muscle Strength and its Relation with LBP in Elite Judokas
Bibliographic record
Abstract
Trunk muscle strength (TMS), especially rotator, is an important factor for both athletic performance and injury risks in judokas. However, the characteristics of TMS that depend on weight categories are still unclear. Although inadequate TMS might cause low back pain (LBP), the relationship between these two variables is not fully understood. In this study, weight category-dependent TMS and its relationship with LBP was investigated in Japanese judokas. The subjects were 66 male collegiate judokas, who were classified into the lightweight (N=14), middleweight (N=29) or heavyweight (N=23) category. The peak torques of extensor, flexor and rotator muscles were measured. The LBP group and non-LBP group were defined by questionnaire. TMS (normalized by body weight) in the heavier weight category showed a lower tendency in comparison with other categories, except for rotator strength. Significant differences were observed in the extensors (90 and 120 °/s; P<0.05) and flexors (60, 90 and 120 °/s; P<0.05). The LBP group (N=9) only showed significantly lower extensor (60 °/s; P<0.05) and right-rotator (60 °/s; P<0.01) strength in the heavyweight category compared with the non-LBP group (N=14). The relative TMS of judokas decreased in the heavyweight category, except for trunk rotator strength. Notably, low trunk extensor and rotator strengths are risk factors for LBP in heavyweight judokas.
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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) | 0.001 | 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".