Comparison of Alternative Methods to Improve Weight-Bearing Sagittal Plane Anterior Leg Rotation
Bibliographic record
Abstract
ABSTRACT: vonGaza, GL, and Chiu, LZF. Comparison of alternative methods to improve weight-bearing sagittal plane anterior leg rotation. J Strength Cond Res 35(12): 3315-3321, 2021-Promoting rearfoot plantar flexion may permit greater sagittal plane anterior leg rotation in weight-bearing tasks. Anterior leg rotation, where the proximal tibia translates forward, is required for tasks such as squatting and landing from a jump. Twenty-eight individuals with less than 25° anterior leg rotation during a weight-bearing lunge test were enrolled and randomly assigned to self-massage and stretching only (n = 15; 14 subjects retained) or self-massage and stretching plus gastrocnemius exercise (n = 13). Anterior leg rotation was assessed during a weight-bearing lunge test and a partial squat; 95% confidence interval (95% CI) of the change score and Cohen's d effect size were calculated. Anterior leg rotation in the weight-bearing lunge increased in the self-massage and stretching only (left: 95% CI [2.1°-5.4°], d = 1.14; right: 95% CI [2.3°-6.0°], d = 1.22) and self-massage and stretching plus gastrocnemius exercise (left: 95% CI [2.3°-7.5°], d = 1.71; right: 95% CI [4.2°-8.6°], d = 1.48) groups. There were no changes in anterior leg rotation in the partial squat for self-massage and stretching only (left: 95% CI [-1.2° to 2.5°], d = 0.15; right: 95% CI [-0.5° to 2.6°], d = 0.24) or self-massage and stretching plus gastrocnemius exercise (left: 95% CI [-0.2° to 4.8°], d = 0.55; right: 95% CI [-0.2° to 4.0°], d = 0.59) groups. Increases in anterior leg rotation in the weight-bearing lunge may be due to decreased passive stiffness in the plantar structures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".