The influence of pain catastrophizing on pain and function after knee arthroplasty in knee osteoarthritis
Bibliographic record
Abstract
Pain catastrophizing is an exaggerated focus on pain sensations. It may be an independent factor influencing pain and functional outcomes of knee arthroplasty. We aimed to evaluate the association between pre-operative pain catastrophizing with pain and function outcomes up to one year after knee arthroplasty. We used data from a cohort study of patients undergoing primary knee arthroplasty (either total or unicompartmental arthroplasty) for knee osteoarthritis. Pain catastrophizing was assessed pre-operatively using the Pain Catastrophizing scale (PCS). Other baseline variables included demographics, body mass index, radiographic severity, anxiety, depression, and knee pain and function assessed using the Western Ontario and McMaster University Index (WOMAC). Patients completed the WOMAC at 6- and 12-months after arthroplasty. WOMAC pain and function scores were converted to interval scale and the association of PCS and changes of WOMAC pain and function were evaluated in generalized linear regression models with adjustment with confounding variables. Of the 1136 patients who underwent arthroplasty (70% female, 84% Chinese, 92% total knee arthroplasty), 1102 and 1089 provided data at 6- and 12-months post-operatively. Mean (± SD) age of patients was 65.9 (± 7.0) years. PCS was associated with a change in WOMAC pain at both 6-months and 12-months (β = - 0.04, 95% confidence interval: - 0.06, - 0.02; P < 0.001) post-operatively after adjustment in multivariable models; as well as change in WOMAC function at 6-months and 12-months. In this large cohort study, pre-operative pain catastrophizing was associated with lower improvements in pain and function at 6-months and 12-months after arthroplasty.
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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 | 0.010 |
| 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) | 0.001 | 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".