Stability of Measures of Pain Catastrophizing and Widespread Pain Following Total Knee Replacement
Bibliographic record
Abstract
OBJECTIVE: Pain catastrophizing and widespread pain are predictors of pain chronicity/severity. Gaps remain in our understanding of the extent to which each is a stable (trait) or dynamic (state) variable. We undertook this study to assess the stability of each variable from before to after total knee replacement (TKR) and whether changes are explained by pain improvements. METHODS: We used data from a prospective study of TKR recipients ages ≥40 years. Questionnaires included body pain diagrams assessing widespread pain, the Pain Catastrophizing Scale (PCS), and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale. We divided subjects into widespread pain groups (0 versus 1-2 versus ≥3 pain regions) and into low and high PCS score groups (<16 versus ≥16). We assessed changes in group membership from pre-TKR to 12 months post-TKR, then compared these changes between subjects with most and least WOMAC pain improvement. RESULTS: A total of 176 subjects completed scales at both time points; 64% were female, the mean age was 66 years, and baseline median WOMAC pain score was 40. In all, 71% of subjects in the high PCS score group improved to join the low PCS score group at follow-up. While 73 subjects (41%) changed widespread pain group, they were similarly likely to worsen and to improve. We found a statistically significant positive association of improvement in WOMAC pain score with improvement in PCS score (r = 0.31), but not widespread pain (r = -0.004). CONCLUSION: The PCS score reflects state-like aspects of catastrophizing that diminish along with pain. In contrast, widespread pain scores worsened and improved equally often, regardless of knee pain relief. The findings urge caution in interpreting PCS score and widespread pain as trait measures in musculoskeletal research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".