A geometric approach to study the contact mechanisms in the patellofemoral joint of normal versus patellofemoral pain syndrome subjects
Bibliographic record
Abstract
The biomechanics of the patellofemoral (PF) joint is complex in nature, and the aetiology of such manifestations of PF instability as patellofemoral pain syndrome (PFPS) is still unclear. At this point, the particular factors affecting PFPS have not yet been determined. This study has two objectives: (1) The first is to develop an alternative geometric method using a three-dimensional (3D) registration technique and linear mapping to investigate the PF joint contact stress using an indirect measure: the depth of virtual penetration (PD) of the patellar cartilage surface into the femoral cartilage surface. (2) The second is to develop 3D PF joint models using the finite element analysis (FEA) to quantify in vivo cartilage contact stress and to compare the peak contact stress location obtained from the FE models with the location of the maximum PD. Magnetic resonance images of healthy and PFPS subjects at knee flexion angles of 15°, 30° and 45° during isometric loading have been used to develop the geometric models. The results obtained from both approaches demonstrated that the subjects with PFPS show higher PD and contact stresses than the normal subjects. Maximum stress and PD increase with flexion angle, and occur on the lateral side in healthy and on the medial side in PFPS subjects. It has been concluded that the alternative geometric method is reliable in addition to being computationally efficient compared with FEA, and has the potential to assess the mechanics of PFPS with an accuracy similar to the FEA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".