Assessment of postoperative health functioning after knee arthroplasty in relation to pain catastrophizing: a 6-month follow-up cohort study
Bibliographic record
Abstract
BACKGROUND: Knee arthroplasty (KA) is a typically successful surgical procedure commonly performed to alleviate painin participants with end-stage knee osteoarthritis. Despite its beneficial effects, a significant proportion of individuals with KA continue experiencing persistent pain and functional limitations. The purpose of this study was to assess the postoperative outcomes after KA in relation to postoperative pain catastrophizing. METHODS: Participants were recruited at a domiciliary physiotherapy service, using a prospective, observational, hypothesis-generating cohort design. Participants were divided into two groups based on their Pain Catastrophizing Scale (PCS) total score (50th percentile), which resulted in high and low PCS groups. The primary outcome measure was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). In addition, quality of life, walking speed, physical performance, range of motion, and pain were measured. Outcome measures were collected at baseline (1 week postoperatively) and at follow-up (1, 3, and 6 months postoperatively). RESULTS: > 0.05). CONCLUSION: The results of the present study suggest that participants with high postoperative pain catastrophizing might have poorer outcomes during the rehabilitation process after KA. Future work should seek to clarify if this relationship is causal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".