Impact of knee geometry on joint contact mechanics after meniscectomy
Bibliographic record
Abstract
Finite element modeling has served as a cornerstone in understanding knee joint mechanics post-meniscectomy, yet the influence of varying knee geometries remains unknown. The present study aimed to fill that gap by employing statistical shape modeling to generate knee models from MRI data of 31 human knees, capturing the population's knee size and shape variations. Finite element simulations were conducted to replicate intact, partial, and total medial meniscectomy conditions during standing. The results revealed a substantial shift in load distribution from the medial to lateral compartment following medial meniscectomy with its magnitude depending on knee geometry. Cartilages experienced variable degrees of pressure changes at different sites, which could also be different for fluid and contact pressures. While changes in joint size led to somewhat predictable alterations in contact pressure, variations in joint shape resulted in unexpected changes in contact and fluid pressures, emphasizing the need for computational simulations. The average knee geometry exhibited the lowest contact and fluid pressures under the given loading and boundary conditions, in contrast to knees with shapes deviating from the average. This study highlights the significance of individual knee shape in the biomechanical outcome of meniscectomy, potentially explaining the variability in clinical outcomes observed post-surgery.
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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 | 0.003 |
| 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 | 0.001 | 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".