Patient satisfaction after total knee arthroplasty: an Asian perspective
Bibliographic record
Abstract
INTRODUCTION: Total knee arthroplasty (TKA) is an effective method for alleviating pain and restoring knee function in patients with severe osteoarthritis. However, despite the improvements in surgical technique and postoperative care, it has been reported that up to 19% of patients are dissatisfied after their operations. The aim of this study was to evaluate patient satisfaction levels after TKA in an Asian cohort, as well as assess the correlation between patient satisfaction levels and the results of traditional physician-based scoring systems. METHODS: The medical data of 103 Asian patients who underwent 110 TKAs between December 2008 and June 2009 were obtained from our hospital's Joint Replacement Registry. The minimum follow-up period was one year and patient expectations were assessed before TKA. Patient satisfaction was assessed postoperatively using a 5-point Likert scale. Reasons for patient dissatisfaction were recorded. Standardised instruments (e.g. the Knee Society Score, the Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] and the generic Short Form-36 health survey) were used to assess the patient's functional status and the severity of symptoms pre- and postoperatively. RESULTS: Among the 110 TKAs performed, 92.8% resulted in patient satisfaction. Patient satisfaction correlated with postoperative WOMAC function scores (p = 0.028), postoperative WOMAC final scores (p = 0.040) and expectations being met (p = 0.033). CONCLUSION: Although there was a high level of patient satisfaction following TKA in our cohort of Asian patients, a significant minority was dissatisfied. Patient satisfaction is an important outcome measure and should be assessed in addition to traditional outcome scores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".