Analysis Techniques for Congruence of the Patellofemoral Joint
Bibliographic record
Abstract
Quantifying joint congruence may help to understand the relationship between joint function and health. In previous studies, a congruence index (CI) has been used to define subject-specific joint congruence. However, the sensitivity of the CI algorithm to surface representation was unknown. The purpose of this study was to assess the effects of applying five modifications (M1-M5) to the CI algorithm to determine whether the magnitude and variability of the patellofemoral CI is dependent on the surface representation used. The five modifications focused on calculating the CI based on the principal curvature (M1) at the centroid of the contact region, (M2) using an root mean square value for the contact region, (M3) using a mean value for the contact region, (M4) using all digitized points of the patellar surface, and (M5) using all digitized points in contact. The CI found using the contact area (M1, M2, M3, and M5) provides a local measure for congruence, which was shown to increase (decreasing CI) with increasing joint angle. In ten healthy subjects measured with magnetic resonance (MR) images, the patellofemoral joint became significantly more congruent as the knee angle increased from 15 deg to 45 deg using method M5. The magnitude and variability of the patellofemoral CI was dependent on the surface representation used, suggesting that standardization of the surface representation is important to provide a consistent measure. Specifically, M5 provides a local measure of joint congruence, which can account for joint position and orientation. M5 balances the ability to detect differences in congruence between knee angles without introducing high variability.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".