Prediction of thigh-calf contact parameters from anthropometric regression
Bibliographic record
Abstract
High knee flexion postures are common in industry and cultural practices, but the mechanical effect of intersegmental force, particularly thigh-calf contact force, on knee joint compressive force is poorly understood. Although some studies have measured thigh-calf contact parameters occurring in a number of high flexion postures, joint contact modeling would benefit from efforts to predict thigh-calf contact force for use in computational models. Therefore, this study assessed the strength of correlations and linear multiple regression models on the following five high flexion thigh-calf contact parameters in a young, healthy population: the onset angle of thigh-calf contact, maximum flexion angle, total thigh-calf contact force, center of force, and contact area at maximum flexion. Regressions used anthropometric values (and in some cases, maximum flexion angle). Overall, maximum flexion angle and center of force location had the most significant correlates and strong linear fits with regressive models. Thigh-calf contact onset, total force magnitude, and contact area had only moderate to weak relationships. An exploratory attempt at regression using grouped movements into gross patterns (e.g. two types of squatting were grouped into a general squatting category) using maximum flexion angle and center of force location as dependent variables resulted in similar model fitting. These findings suggest that a causal relationship between select anthropometrics and lower limb range of motion may exist, but further exploration is necessary to determine clinically reliable predictive models.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".