Participation in a Preoperative Patient Education Session Is a Significant Predictor of Better WOMAC Total Index Score and Higher EQ-5D-5L Health Status Index 1 Year After Total Knee and Hip Arthroplasties
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to investigate whether patient-specific factors, preoperative patient-reported outcome measures, and participation in a preoperative patient education session significantly predict 1-yr Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score and EuroQol 5 Dimensions 5 Levels (EQ-5D-5L) health status index of patients who underwent total hip or knee arthroplasties within an enhanced rehabilitation program. DESIGN: This is a retrospective observational cohort study. The inclusion criteria were met by 676 (373 total hip arthroplasties and 303 total knee arthroplasties) patients. Two multiple regression models were carried out to estimate the contributions of nine potential predictors. RESULTS: Younger age (P = 0.006), higher preoperative EQ-5D-5L index (P = 0.004), lower patient clinical complexity level (P = 0.001), lower preoperative WOMAC total score (P < 0.001), preoperative patient education session (P = 0.004), and submitting for total hip arthroplasty (P < 0.001) were significant predictors of better 1-yr WOMAC total score. Higher preoperative EQ-5D-5L index (P < 0.001), lower patient clinical complexity level classification (P < 0.001), lower preoperative WOMAC total score (P = 0.009), preoperative patient education session (P = 0.04), and submitting for total hip arthroplasty (P = 0.01) were significant predictors of higher 1-yr EQ-5D-5L health status index. CONCLUSIONS: Better baseline patient-reported outcome measure scores, less comorbidities, younger age, submitting for total hip arthroplasty, and attending a preoperative patient education session were significant predictors of better WOMAC total scores and higher EQ-5D-5L health status index 1 yr after total hip or total knee arthroplasties.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".