The analysis of knee joint loading during drop landing from different heights and under different instruction sets in healthy males
Bibliographic record
Abstract
BACKGROUND: Mechanical loading during exercise has been shown to promote tissue remodeling. Safe and accessible exercise may be beneficial to populations at risk of diminished bone and joint health. We examined the effect of drop height and instruction on knee loading during a drop-landing task and proposed a task that makes use of drop heights that may be appropriate for rehabilitation purposes and functional in daily life to examine transient knee joint loads. METHODS: Twenty males (22.0 ± 2.8 years) performed drop landings from 22 cm (low) and 44 cm (high) heights, each under three instructions: "land naturally" (natural), "softly" (soft), and "stiffly" (stiff). Knee compression force and external flexion moment were estimated using three-dimensional inverse dynamics and normalized to body mass. RESULTS: Peak knee compression force was larger (p < 0.001) for high (17.8 ± 0.63 N/kg) than low (14.8 ± 0.61 N/kg) heights. There was an increase (p < 0.001) in the knee compression force across soft (11.8 ± 0.40 N/kg), natural (17.0 ± 0.62 N/kg), and stiff (20.2 ± 0.67 N/kg) instructions. Peak knee flexion moment in high-natural (2.12 ± 0.08 Nm/kg) was larger (p < 0.001) than in high-soft (1.88 ± 0.08 Nm/kg), but lower than in high-stiff (2.23 ± 0.08 Nm/kg). No differences in peak knee flexion moment were observed across instructions for the low height. CONCLUSIONS: We propose a drop-landing task that creates a scalable increase in knee compression loading. The absence of increased knee flexion moment with drop from the low height, compared to high, suggests that individuals could perform the task without incremental risk of knee injury. This task could be used in future studies to examine the effect of acute bouts of mechanical loading on bone and cartilage metabolism.
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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) | 0.001 | 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".