Effects of lead leg selection on bilateral landing force‐time characteristics: Return to sport testing implications
Bibliographic record
Abstract
We quantified the effect of lead leg selection on landing force–time characteristics during a vertical drop landing (VDL) initiated with a step‐off. Plyometric‐trained subjects (male: n = 8; female: n = 5; age =23 ± 3.3 years; body mass =74.4 ± 11.2 kg) performed 6 step‐off‐initiated VDLs from a 45‐cm box (n = 3/lead leg). Pooled and lead leg stratified between‐limb comparisons of limb‐specific initial ground contact timing, peak vertical ground reaction force (Fzpeak), and early landing‐phase impulse (Impulse100ms) were assessed by a two‐factor, within‐subject ANOVA, and limb symmetry indexes were calculated (α = 0.05). Pooled data showed that the lead leg made contact prior to the trail leg and contributed a greater fraction to Fzpeak compared with the trail leg. Stratifying trials by lead leg selection led to significant between‐limb differences in Fzpeak (p < 0.05) and Impulse100ms (p < 0.01) with the right leg leading but not with the left leg leading. Lead leg selection in step‐off‐initiated VDLs influenced landing performance and limb symmetry indexes of variables associated with lower limb injury risk, suggesting the need to control for lead leg selection in these tasks. A step‐off may not be a suitable technique to initiate landing tasks when assessing limb symmetry.
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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 | 0.002 |
| 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 | 0.000 | 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".