The effects of a simulated occupational kneeling exposure on squat mechanics and knee joint load during gait
Bibliographic record
Abstract
Occupational kneeling is associated with an increased risk for tibiofemoral knee osteoarthritis. Forces on the knee in the kneeling posture, as well as the greater incidence of meniscus tears among workers, likely contribute to the increased risk. We hypothesise that an additional mechanism may contribute - altered neuromuscular control due to prolonged high knee flexion. Forty participants (20 male, 20 female) completed an evaluation of gait and squatting before, immediately following, and 30 min following a 30 min simulated occupational kneeling exposure. An increase in the peak external knee adduction moment and a delay in vastus medialis activation onset during walking were observed post-kneeling, as well as increased frontal plane knee motion during squatting. This was the first investigation to find changes in high flexion transitions as a result of kneeling. Greater frontal plane knee motion may increase the risk for meniscal tears, and subsequently, knee osteoarthritis. Practitioner Summary: A 30 min simulated occupational kneeling exposure resulted in small but significant gait changes. The greatest effect was on frontal plane knee movement during squatting, which is especially relevant to occupations requiring frequent kneeling/squatting. This increased motion may indicate an increased risk of injury, which supports a link to knee osteoarthritis.
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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 | 0.000 | 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) | 0.002 | 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".