Preoperative low scores of Life Satisfaction Rating predicts poor outcomes after total knee arthroplasty: a prospective observational study
Bibliographic record
Abstract
BACKGROUND: Despite the continued improvement in the surgical techniques during primary total knee arthroplasty (TKA), literatures indicate that up to 10 to 20% patients are not satisfied with their outcomes. Psychological factors in this dissatisfaction are yet to be clearly identified. The aim of this study is to develop a method to assess whether the patient's current mental state is suitable enough to accept a TKA surgery. METHODS: Preoperative demographic and clinical data of 532 patients who underwent TKA were prospectively obtained from January 2012 until December 2016. We recorded the scores evaluated by SF-36 questionnaire and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) preoperatively and 1 year postoperatively. Preoperative Life Satisfaction Rating (LSR) is emphatically evaluated. RESULTS: Poor preoperative score of LSR was a significant predictor of dissatisfaction after TKA. Patients with low LSR reported significant pain and stiffness, although there was no remarkable effect on functionality of the replaced joint. The results also showed that age and BMI were not strong predictors of satisfaction in TKA. CONCLUSION: Our outcomes can help clinicians evaluate whether a patient's current mental status is favorable for TKA. If patients have extreme low scores of LSR (less than 10), a psychological intervention should be recommended for better satisfaction following a TKA surgery. This would also allow surgeons to individually assess the risks and benefits of surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".