DO PERSONALITY TRAITS INFLUENCE PATIENT SATISFACTION AFTER TOTAL KNEE ARTHROPLASTY?
Bibliographic record
Abstract
Ten to twenty percent of patients are dissatisfied with the clinical result after total knee arthroplasty (TKA). Aim of this study was to investigate the impact of personality traits on patient satisfaction and subjective outcome of TKA. We investigated 80 patients with 86 computer navigated TKAs (Emotion®, B Braun Aesculap) and asked for patient satisfaction. We divided patients into two groups (satisfied or dissatisfied). 12 personality traits were tested by an independent psychologist, using the Freiburg Personality Inventory (FPI-R). Postoperative examination included Knee Society Score (KSS), Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and the Visual Analogue Scale (VAS). Radiologic investigation was done in all patients. 84% of our patients were satisfied, while 16% were not satisfied with clinical outcome. The FPI-R showed statistically significant influence of four personality traits on patient satisfaction: life satisfaction (ρ = 0.006), performance orientation (ρ =0.015), somatic distress (ρ = 0.001), and emotional stability (ρ = 0.002). All clinical scores (VAS, WOMAC, and KSS) showed significant better results in the satisfied patient group. Radiological examination showed optimal alignment of all TKAs. There were no complications requiring revision surgery in both groups. The results of our study show that personality traits may influence patient satisfaction and clinical outcome after TKA. Thus, patients personality traits may be a useful predictive factor for postoperative satisfaction after TKA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".