Comparison of patellar resurfacing versus nonresurfacing in total knee arthroplasty.
Bibliographic record
Abstract
OBJECTIVES: To determine whether resurfacing the patellar component during total knee replacement (TKR) influences the clinical outcome. DESIGN: A retrospective study of data gathered prospectively during the recovery course of patients who underwent TKR with or without patellar resurfacing. SETTING: Victoria General Hospital, Halifax, NS. PATIENTS: One hundred and eighty-five patients operated on between 1992 and 1995. The inclusion criteria were (a) osteoarthritis, (b) replacement carried out by 2 independent surgeons, (c) no comorbid illness such as rheumatoid arthritis, cancer or infection, (d) pre- and postoperative attendance at the assessment clinics. INTERVENTION: TKR with (45) or without (140) patellar replacement. MAIN OUTCOME MEASURES: Range of motion (ROM), pain assessment, Hospital Severity Score (HSS) and complications. RESULTS: There was no statistical difference between the 2 groups with respect to ROM, pain, HSS and complications postoperatively. CONCLUSIONS: Resurfacing the patella during TKR does not seem to influence the clinical outcome with respect to ROM, pain and overall complications. The decision should be based on individual criteria, depending on the preoperative and intraoperative findings. Randomized clinical trials assessing ROM, pain, complications and cost-effectiveness with long-term follow-up are necessary to further investigate this controversial issue.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".