Knee coordination during landing in ballet-trained female dancers and non-dancers
Bibliographic record
Abstract
Dance is a prevalent activity in females who are at a greater risk for traumatic knee injuries compared with males. However, dancers have a lower injury incidence than other athletes, and landing techniques obtained from training may limit accessory frontal plane movement. Frontal and sagittal plane kinematics, kinetics, and knee coordination patterns were compared between 18 ballet-trained female dancers and 18 non-dancers throughout the landing phase of a drop vertical jump. Dancers landed with greater peak knee flexion (-113.23 ± 15.1° vs. -99.3 ± 16.2°; p = 0.013, d = 0.890) and lower peak knee abduction (-1.15 ± 4.28° vs. -7.26 ± 6.26°; p = 0.002, d = 1.145) compared with controls. Dancers had smaller external knee flexion moments (0.14 ± 0.02 vs. 0.17 ± 0.05 BWxHt, p = 0.043, d = 0.702), smaller peak external knee abduction moments (0.024 ± 0.011 vs. 0.0036 ± 0.014 BWxHt, p = 0.046, d = 0.680), and smaller peak vertical ground reaction force (1.57 ± 0.22 vs. 1.92 ± 0.43 BW, p = 0.005, d = 1.008) compared with controls. Dancers also utilized a coordination pattern that included less in-phase motion (p < 0.001) between the sagittal and frontal knee coupling and more exclusive shank motion (p = 0.027) between the thigh and shank in the frontal plane compared with controls. There were no differences between the groups in exclusive sagittal plane (p = 0.386), exclusive frontal plane (p = 0.708), or anti-phase (p = 0.443) motion between the sagittal and frontal knee coupling compared with controls. Dancers undergo extensive landing training, which may contribute to less frontal plane knee motion. As such, dancers' ability to flex the knee without additional and simultaneous knee abduction during landing may help to mitigate their acute knee injury risk.
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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 | 0.001 | 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) | 0.002 | 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".