The WOMAC score can be reliably used to classify patient satisfaction after total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: The primary aim of this study was to define a classification in the WOMAC score after total knee arthroplasty (TKA) according to patient satisfaction. The secondary aims were to describe patient demographics for each level of satisfaction. METHODS: A retrospective cohort consisting of 2589 patients undergoing a primary TKA were identified from an established arthroplasty database. Patient demographics, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and short form (SF) 12 scores were collected pre-operatively and 1 year post-operatively. In addition, patient satisfaction was assessed at 1 year with four responses: very satisfied, satisfied, dissatisfied or very dissatisfied. Receiver operating characteristic (ROC) curves were used to identify values in the components and total WOMAC scores that were predictive of each level of satisfaction, which were used to define the categories of excellent, good, fair and poor. RESULTS: At 1 year, there were 1740 (67.5%) very satisfied, 572 (22.2%) satisfied, 190 (7.4%) dissatisfied and 76 (2.9%) very dissatisfied patients. ROC curve analysis identified excellent, good, fair and poor categories for the pain (> 78, 59-78, 44-58, < 44), function (> 72, 54-72, 41-53, < 41), stiffness (> 69, 56-69, 43-55, < 43) and total (> 75, 56-75, 43-55, < 43) WOMAC scores, respectively. Patients with lung disease, diabetes, gastric ulcer, kidney disease, liver disease, depression, back pain, with worse pre-operative functional scores (WOMAC and SF-12) and those with less of an improvement in the scores, had a significantly lower level of satisfaction. CONCLUSION: This study has defined a post-operative classification of excellent, good, fair and poor for the components and total WOMAC scores after TKA. The predictors of level of satisfaction should be recognised in clinical practice and patients at risk of a lower level of satisfaction should be made aware in the pre-operative consent process. LEVEL OF EVIDENCE: III.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".