The Influence of the Practiced Karate Style on the Dexterity and Strength of the Hand
Bibliographic record
Abstract
Background: The need for a strong grip in ‘ground’ martial arts is undisputed, but it is not obvious in karate. It may be expected that in the case of advanced karate fighters where dynamic combat movements dominate, the level of speed skills will be high. However, does the karate style affect the Ditrich rod dexterity and the strength of the players’ handshake? Methods: 39 participants were analyzed, all of whom were elite karate fighters—21 in the Kyokushin style (age: 31.4 ± 6.3; body weight: 77.2 ± 18.2 kg) and 18 in the Shotokan style (age: 23.3 ± 11.8 years; body weight: 70.9 ± 14.2 kg). They performed the following: a test of reaction speed and dexterity with a Ditrich rod, and a hand grip strength test with a dynamometer. Results: The data shows that there is no difference in the Ditrich rod test for both the left and right hand among the analyzed Kyokushin and Shotokan fighters. Significant differences were recorded in the grip strength of both hands (p < 0.05). There is a positive correlation between the strength of the grip on both hands (r = 0.593; p < 0.05). Discussion: Kyokushin-style karate players have a higher grip strength than those trained in the Shotokan style. Perhaps this is due to differences in the preparation for fights. The analysis showed no statistical significance in the Ditrich rod test. It is probable that the level of dexterity in karate is independent of the training canon.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".