Lower Extremity Coordination Strategies To Mitigate Dynamic Knee Valgus During Landing In Males And Females
Bibliographic record
Abstract
Frontal and sagittal plane landing biomechanics differ between sexes but reported values do not account for simultaneous segment or joint rotations necessary for a coordinated landing. PURPOSE: To compare frontal and sagittal plane coordination patterns, joint angles, and moments throughout the landing phase of a drop vertical jump. METHODS: 28 males and 28 females performed a drop vertical jump from a 30 cm box placed half their height from the force platforms. A modified vector coding technique was used to compare frontal and sagittal plane thigh/shank segment and relative knee angle coordination patterns via Mann-Whitney U Tests. Continuous and discrete angles and moments were compared via statistical parametric mapping and independent t-tests, respectively. RESULTS: Females landed with less isolated thigh abduction (Z = -2.36, p = .018), more in-phase motion (Z = 3.33, p < .001), and more isolated shank adduction (Z = 2.20, p = .028) between the thigh and shank in the frontal plane compared with males. Females landed with less in-phase (Z = -2.51, p = .012) and more anti-phase motion (Z = 2.35, p = .019) between the thigh and shank in the sagittal plane compared with males. Females landed with less isolated knee flexion (Z = -3.28, p = .001) and more anti-phase motion (Z = 3.95, p < .001) between the sagittal and frontal plane knee coupling compared with males. Waveform and discrete metric analyses revealed females land with less thigh abduction from 20%-100% (p < .001) and more shank abduction from 0-100% (p < .001) of landing, smaller knee adduction at initial contact (p = .002, d = .882), greater peak knee abduction angles (p = .015, d = .669), smaller knee flexion angles at initial contact (p = .035, d = -.579) and peak (p = .034, d = -.584), smaller peak knee flexion moments (p < .001, d = 1.224), greater knee abduction angles from 0-13% (p = .040) and 19-30% (p = .042) of landing, smaller knee abduction moments from 0-1% (p = .049) of landing, and smaller knee flexion moments from 35-100% (p < .001) of landing compared with males. CONCLUSION: Females utilize greater frontal plane knee motion compared with males, which may be due to different inter-segmental joint coordination and smaller knee flexion angles. Larger knee abduction angles and greater knee adduction motion in females are due to aberrant shank abduction rather than thigh adduction.
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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 | 0.000 |
| 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) | 0.004 | 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".