A new prediction model for patient satisfaction after total knee arthroplasty and the roles of different scoring systems: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Although total knee arthroplasty (TKA) is an efficacious treatment for end-stage osteoarthritis, ~20% of patients are dissatisfied with the results. We determined which factors contribute to patient satisfaction and compared the various scoring systems before and after surgery. METHODS: In this retrospective cohort study, 545 patients were enrolled and evaluated preoperatively and 1 year postoperatively. Patient demographics, as well as scores for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Short Form (SF)-12, and 1989 Knee Society Clinical Rating System (1989 KSS), were recorded preoperatively and postoperatively. The possible predictors were introduced into a prediction model. Scores for overall satisfaction and the 2011 Knee Society Score (2011 KSS) were also assessed after TKA to identify the accuracy and agreement of the systems. RESULTS: There were 134 male patients and 411 female patients, with an overall prevalence of satisfaction of 83.7% 1 year after surgery. A history of surgery (p < 0.001) and the 1989 KSS and SF-12 were of the utmost importance in the prediction model, whereas the WOMAC score had a vital role postoperatively (change in WOMAC pain score, p < 0.001; change in WOMAC physical function score, p < 0.001; postoperative WOMAC pain score, p = 0.004). C-index of model was 0.898 > 0.70 (95% confidence interval (CI): 0.86-0.94). The Hosmer-Lemeshow test showed a p value of 0.586, and the AUC of external cohort was 0.953 (sensitivity=0.87, specificity=0.97). The agreement between the assessment of overall satisfaction and the 2011 KSS satisfaction assessment was general (Kappa=0.437 > 0.4, p < 0.001). CONCLUSION: A history of surgery, the preoperative 1989 KSS, and the preoperative SF-12 influenced patient satisfaction after primary TKA. We recommend the WOMAC (particularly the pain subscale score) to reflect overall patient satisfaction postoperatively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".