Effects of socioeconomic status on patients' outcome after total knee arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To identify whether patients in lower socioeconomic groups had worse pain and functional levels prior to total knee arthroplasty and then establish whether these patients had poorer post-operative outcomes following total knee arthroplasty. METHOD: Data was obtained from a prospective observational study of 974 patients undergoing primary total knee arthroplasty for osteoarthritis. The study was undertaken in 13 centers in 4 countries. Pre-operative data was collected and patients were followed for 2 years post-operatively. Pre-operative details of the patients' demographics; socioeconomic status (SES) (education and income); height; weight and co-morbid conditions were obtained. The WOMAC scores were obtained preoperatively and during follow-up. RESULTS: Using multivariate linear regression analysis, patients with a lower income had a significantly worse pre-operative WOMAC Pain (P = 0.021) and function score (P = 0.039) than those with higher incomes. However, income did not have a significant impact on outcome at final follow-up after adjusting for other significant covariates. Level of education did not correlate with pre-operative scores or with outcome at any time during follow-up. CONCLUSION: Across all four countries, patients with lower incomes appeared to have a greater need for total knee arthroplasty. However, level of income and educational status did not appear to affect the final outcome following total knee arthroplasty. Patients with lower incomes appeared able to compensate for their worse pre-operative score and obtain similar outcomes post-operatively. These findings are in contrast to studies on other medical conditions and surgical interventions, in which a lower SES has been found to have a negative impact on patient outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".