Predicting the Outcome of Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: The relief of pain and the restoration of functional activities are the main outcomes of primary total knee arthroplasty for the treatment of osteoarthritis. This paper examines the preoperative predictors of pain and functional outcome at one and two years following total knee arthroplasty. METHODS: Patients were recruited for a prospective observational study of primary total knee arthroplasty for the treatment of osteoarthritis from centers in the United States, the United Kingdom, and Australia. Research assistants recruited the patients and collected the clinical history and physical examination data preoperatively and at three, twelve, and twenty-four months postoperatively. The Western Ontario and McMaster University Osteoarthritis Index (WOMAC), Short Form-36 (SF-36), and demographic data were obtained by self-administered patient questionnaires. RESULTS: We recruited 860 patients and obtained one-year WOMAC data on 759 patients (88%) and two-year data on 701 (82%). The mean age was seventy years, and 59% of the patients were female. Using hierarchical regression models, we found that the most significant preoperative predictors of worse scores on the pain and function domains of the WOMAC scale and on the physical functioning domain of the SF-36 at one and two years postoperatively were low preoperative scores, a higher number of comorbid conditions, and a low SF-36 mental health score. After adjusting for these predictors, we found that the functional status of the patients from the United Kingdom was significantly worse than that of the patients from the other countries and the difference was clinically important at both the one-year and two-year follow-up examination (p < 0.0005). The mean WOMAC pain scores for the three countries were not significantly different at one year, and, although they were significantly different at two years (p = 0.025), the difference was not clinically important. CONCLUSIONS: Patients who have marked functional limitation, severe pain, low mental health score, and other comorbid conditions before total knee arthroplasty are more likely to have a worse outcome at one year and two years postoperatively. After adjusting for these predictors, it was found that patients from the United Kingdom had significantly worse functional outcomes but similar pain relief compared with those from the United States and Australia.
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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".