Does the degree of intraoperatively identified cartilage loss affect the outcomes of primary total knee arthroplasty without patella resurfacing? A prospective comparative cohort study
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate whether the degree of patellar cartilage loss confirmed during index surgery affects the clinical and radiologic outcomes of total knee arthroplasty (TKA) performed without patellar resurfacing. METHODS: We prospectively divided 2012 patients with a minimum follow-up of 12 months into two groups according to intraoperatively graded cartilage lesions graded using the International Cartilage Repair Society (ICRS) system: group 1, grades 0‒2 (n = 110); group 2, grades 3‒4 (n = 102). Relevant locations, such as medial, lateral, or both facets of the patella, were also assessed. Clinical outcomes were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index, Feller's patella score, and Kujala anterior knee pain score. Radiographic outcomes included patellar tilt angle and lateral patellar shift on Merchant's view. RESULTS: Clinical and radiographic outcomes were not significantly different between the two groups. No patient underwent secondary patellar resurfacing. Although the lateral facet was significantly more involved, there were no significant differences in outcomes. CONCLUSIONS: The degree of intraoperatively identified patellar cartilage loss did not affect the short-term outcomes following primary TKA without patellar resurfacing. Level of evidence II: Prospective comparative study.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".