Preoperative cognitive–behavioral therapy for reducing pain catastrophizing and improving pain outcomes after total knee replacement: a randomized clinical trial
Bibliographic record
Abstract
INTRODUCTION: Cognitive-behavioral therapy (CBT) can reduce preoperative pain catastrophizing and may improve postsurgical pain outcomes. We hypothesized that CBT would reduce pain catastrophizing more than no-CBT controls and result in improved pain outcomes. METHODS: The study was a randomized controlled trial of patients undergoing elective total knee arthroplasty between January 2013 and March 2020. In phase 1, the change in pain catastrophizing scores (PCS) among 4-week or 8-week telehealth, 4-week in person and no-CBT sessions was compared in 80 patients with a PCS >16. In phase 2, the proportion of subjects that achieved a 3-month decrease in Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain subscale >4 following 4-week telehealth CBT with no-CBT controls were compared in 80 subjects. RESULTS: In phase 1, 4-week telehealth CBT had the highest completion rate 17/20 (85%), demonstrated an adjusted median reduction in PCS of -9 (95% CI -1 to -14, p<0.01) compared with no-CBT and was non-inferior to 8-week telehealth CBT at a margin of 2 (p=0.02). In phase 2, 29 of 35 (83%) in the 4-week telehealth CBT and 26 of 33 (79%) subjects in the no-CBT demonstrated a decrease in the WOMAC pain subscale >4 at 3 months, difference 4% (95% CI -18% to 26%, p=0.48), despite a median decrease in the PCS for the 4-week CBT and no-CBT group of -6 (-10 to -2, p=0.02). CONCLUSIONS: Our findings demonstrate that CBT interventions delivered prior to surgery in person or via telehealth can reduced PCS scores; however, this reduction did not lead to improved 3-month pain outcomes. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov (NCT01772329, registration date 21 January 2013).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".