The Role of Preoperative Self‐Efficacy in Predicting Outcome after Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to determine if self-efficacy is a significant and independent preoperative predictor of patient-reported pain and function at one year after total knee replacement (TKR). METHODS: Patients listed for a primary TKR because of osteoarthritis were recruited from preoperative assessment clinics at one regional orthopaedic centre. Before surgery, patients completed the Western Ontario and McMasters Osteoarthritis Index (WOMAC) Pain and Function Scale, Pain Self-Efficacy Scale, the Hospital Anxiety and Depression Scale, the Self-Administered Co-morbidity Questionnaire and questions about other painful joints. Patients then completed the WOMAC Pain and Function Scales at one year postoperatively. Regression analysis was performed to determine if self-efficacy was a significant predictor of outcome after TKR. RESULTS: Overall, 251 patients were recruited into this study, and one-year questionnaire data were available for 220 patients. At one year postoperatively, 7% of patients reported severe pain in their replaced knee and 9% reported severe functional limitations. Self-efficacy was found to be a significant preoperative predictor of functional ability, but not pain, at one year postoperatively, after controlling for age, gender, depression, anxiety, number of medical co-morbidities, preoperative knee status and painful joints elsewhere. Significant predictors of postoperative pain were greater anxiety and higher pain severity. Other significant predictors of postoperative disability were greater anxiety, worse functional disability and a greater number of painful joints elsewhere. CONCLUSIONS: The present study demonstrated that self-efficacy is a significant preoperative predictor of patient-reported functional ability at one year after TKR. Future research is needed to assess the impact of interventions for enhancing self-efficacy on patient-reported outcomes after TKR.
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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 | 0.007 |
| 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 | 0.001 |
| 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".