The effect of pain neuroscience education on chronic postsurgical pain after total knee arthroplasty: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Chronic postsurgical pain after total knee arthroplasty (TKA) is frequent and may be reduced by pain neuroscience education (PNE), teaching people about pain from a neurobiological perspective. This study investigated primarily the effectiveness of 2 individual sessions of PNE versus usual care on pain levels 3 months postoperatively in patients undergoing TKA. Secondary outcomes were physical functioning, stiffness, health-related quality of life, pain catastrophizing, attention to pain, and levels of anxiety and depression. METHODS: A prospective single-center, parallel-group randomized controlled trial was undertaken including patients aged 18 years or older scheduled for primary TKA. 68 patients were randomly assigned to PNE or usual care. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score 3 months postoperatively. Outcomes were measured preoperatively, at 2 weeks (acute phase), and at 3 and 12 months postoperatively. RESULTS: We found no statistically significant difference (0.4 points; 95% confidence interval [CI] -1.7 to 2.4) in WOMAC pain scores 3 months after TKA between the PNE and control group. We found a statistically significant difference between the 2 groups for attention to pain at 3 months in favor of PNE (P = 0.02). CONCLUSION: This RCT showed that PNE was not superior to usual care in terms of reducing pain at 3 months after TKA. Attention to pain, as a secondary outcome, was significantly lower in the PNE group compared with usual care. Other secondary outcome measures showed no significant differences.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".