The Nonintuitive Contributions of Individual Quadriceps Muscles to Patellar Tracking
Bibliographic record
Abstract
The purpose of this study was to quantify the contribution of the individual quadriceps muscles to patellar tracking. The individual and/or combined quadriceps muscles were activated in rabbits (n = 6) during computer-controlled flexion/extension of the knee. Three-dimensional patellar tracking was measured for the vastus lateralis, vastus medialis, and rectus femoris when activated alone and when activated simultaneously at different frequencies, producing a range of knee extensor torques. Patellar tracking changed substantially as a function of knee extensor torque and differed between muscles. Specifically, when all quadriceps muscles were activated simultaneously, the patella shifted more medially and proximally and rotated and tilted more medially compared with when vastus lateralis and rectus femoris were activated alone (P < .05), whereas vastus medialis activation alone produced a similar tracking pattern to that observed when all quadriceps muscles were activated simultaneously. Furthermore, patellar tracking for a given muscle condition shifted more medially and proximally and rotated and tilted more medially with increasing knee extensor torques across the entire range of knee joint angles. The authors conclude that patellar tracking depends crucially on knee extensor force/torque and that vastus medialis affects patellar tracking in a distinctly different way than vastus lateralis and rectus femoris, which produce similar tracking patterns.
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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 | 0.001 |
| 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) | 0.001 | 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".