Clinical Study and Finite Element Analysis on the Effects of Pseudo‐Patella Baja After <scp>TKA</scp>
Bibliographic record
Abstract
OBJECTIVE: Pseudo-patella baja (PPB) was one of the complications after total knee arthroplasty (TKA). This complication may be closely related to the occurrence of knee joint movement limitation and pain after TKA. This study aimed to investigate whether PPB affects clinical outcomes after TKA and to study the biomechanical effects of PPB after TKA. METHODS: ) test, and analysis of variance (ANOVA) were performed using GraphPad Prism (Version 8, GraphPad Software, USA). A statistically significant difference was considered at p < 0.05 with bilateral α. RESULTS: The VAS, HSS, WOMAC, EQ-5D-5L, FJS-12, and patient satisfaction scores in the PPB and TPB groups were significantly worse than those in the patella normal (PN) group (p < 0.05). The PPB group found a positive correlation between Blackburne-Peel index (BPI) and FJS-12 score. PPB showed lower contact stress of patellofemoral joint compared to TPB when knee flexion was less than < 90° (p < 0.01), but no significant difference when flexion was more than > 90° (p > 0.05) in the finite element model with Patella baja (PB). The contact area of the patellofemoral joint tended to increase with the deepening of knee flexion, and decreased after reaching the peak value. The contact area of the patellofemoral joint tended to decrease with the increase in patellar height. There was no significant difference in the contact area of the patellofemoral joint among different patellar heights and different degrees of knee flexion (p > 0.05). CONCLUSION: PPB after TKA may increase patellofemoral joint stress and postoperative complications like anterior knee pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".