Can the pre-operative Western Ontario and McMaster score predict patient satisfaction following total hip arthroplasty?
Bibliographic record
Abstract
In this study we evaluated whether pre-operative Western Ontario and McMaster Universities (WOMAC) osteoarthritis scores can predict satisfaction following total hip arthroplasty (THA). Prospective data for a cohort of patients undergoing THA from two large academic centres were collected, and pre-operative and one-year post-operative WOMAC scores and a 25-point satisfaction questionnaire were obtained for 446 patients. Satisfaction scores were dichotomised into either improvement or deterioration. Scatter plots and Spearman's rank correlation coefficient were used to describe the association between pre-operative WOMAC and one-year post-operative WOMAC scores and patient satisfaction. Satisfaction was compared using receiver operating characteristic (ROC) analysis against pre-operative, post-operative and δ WOMAC scores. We found no relationship between pre-operative WOMAC scores and one-year post-operative WOMAC or satisfaction scores, with Spearman's rank correlation coefficients of 0.16 and -0.05, respectively. The ROC analysis showed areas under the curve (AUC) of 0.54 (pre-operative WOMAC), 0.67 (post-operative WOMAC) and 0.43 (δ WOMAC), respectively, for an improvement in satisfaction. We conclude that the pre-operative WOMAC score does not predict the post-operative WOMAC score or patient satisfaction after THA, and that WOMAC scores can therefore not be used to prioritise patient care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".