Maximal isometric handgrip strength: comparison between weight categories and classificatory table for adult judo athletes
Bibliographic record
Abstract
The aims of this study were to compare the maximal isometric handgrip strength of judo athletes from different weight categories and to create a classificatory table for this test.A total of 406 athletes had their maximal isometric handgrip strength measured, following standardized recommendations.Absolute and relative values were calculated for each hand and for the sum of both hands.Weight categories were compared through a one-way analysis of variance, followed by Tukey test.The effect size was determined by partial eta squared, and the relationship between variables was determined using Pearson correlation coefficient.There was a large effect of weight category in absolute handgrip strength for each hand and for the sum of both hands, with lower values for the lighter categories.Conversely, when the relative strength was considered higher values were found for the lighter categories (P< 0.001).Very large and significant positive correlations (P< 0.001) were found between right and left for absolute (r= 0.886) and relative (r= 0.883) handgrip values.Overall, there was an increase in absolute and a decrease in relative handgrip strength across weight categories.These differences found in grip strength in weight categories are probably linked to differences in muscle mass between them.There was a high correlation between each hand for absolute and relative values, which suggests that assessing only one hand may be enough, and therefore a faster way of evaluation.Finally, the normative classificatory table created may serve as a reference for different purposes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".