A Latent Change Score Approach to Understanding Chronic Bodily Pain Outcomes Following Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: The extent to which chronic bodily pain changes following total knee arthroplasty (TKA) is unknown. We determined the extent of chronic bodily pain changes at 1 year following TKA. METHODS: Data from our randomized trial of pain coping skills, which revealed no effect of the studied interventions, were used. The presence and severity of chronic pain in 16 body regions, excluding the surgically treated knee, were determined prior to and 1 year following surgery. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scale was used to quantify the extent of surgical knee pain. Latent change score (LCS) models were used to determine the extent to which true chronic bodily pain scores change after TKA. RESULTS: The mean age of the sample of 367 participants was 63.4 ± 8.0 years, and 247 (67%) were female. LCS analyses showed significant 20% to 54% reductions in pain in the surgically treated lower limb (not including the surgically treated knee), pain in the non-surgically treated lower limb, and whole body pain. In bivariate LCS analyses, greater improvement in the WOMAC pain score, indicating surgical benefit of TKA, led to greater improvement in all 4 bodily pain areas beyond the surgically treated knee, even after controlling for the latent change in pain catastrophizing. CONCLUSIONS: Clinically important chronic bodily pain reductions occurred following TKA and may be causally linked to the surgical procedure. Reduction in chronic bodily pain in sites other than the surgically treated knee is an additional benefit of TKA. LEVEL OF EVIDENCE: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".