A Weighted Objective Function Reduces Estimates of Medial and Lateral Knee Joint Contact Loads During Gait
Bibliographic record
Abstract
Accurate prediction of knee joint contact loading during gait is important for understanding knee pathology and development of suitable clinical interventions. While many researchers have modeled the knee contact loads during level walking, these predictions have ranged from 3.4 [1] to 7 [2] times body weight. Validation of contact loads is difficult; the joint contact load depends not only on readily obtainable external kinematics and reaction forces, but also on the forces generated by muscle and other soft tissues. Recently, an instrumented tibial implant, capable of telemetrically reporting the six degree-of-freedom loading environment of the tibial plateau, was used to tune and validate an EMG-driven model of the lower extremity [3]. Recognizing the value of these in vivo data, and the limitations of existing knee models, these researchers devised the Grand Challenge Competitions to Predict In Vivo Knee Loads.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".