Understanding the patellofemoral joint in total knee arthroplasty
Bibliographic record
Abstract
Total knee arthroplasty (TKA) is one of the most successful procedures in orthopedic surgery. Nevertheless, postoperative patellofemoral complications remain a challenging problem, affecting a substantial proportion of patients. Complications involving the patellofemoral joint (PFJ) can occur in both resurfaced and nonresurfaced patellae. Types of PFJ complications include anterior knee pain, maltracking, fracture, avascular necrosis and patellar clunk. The causes of patellofemoral complications can be categorized into patient-, surgeon- and implant-related factors. Patient characteristics such as female sex, young age, depression and increased body mass index have been linked with increased complications. Important technical considerations to avoid complications include achieving appropriate rotational alignment of the femoral and tibial components, maintaining joint line height, medializing the patellar button and avoiding “overstuffing” the PFJ. Component design features such as conformity, shape and depth of the femoral trochlea have also been shown to be important. Although the cause of patellofemoral complications after TKA may sometimes be unknown, it remains important to minimize errors that can lead to these complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".