Preoperative Home-Based Multimodal Physiotherapy in Patients Scheduled for a Knee Arthroplasty Who Catastrophize About Their Pain: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Chronic pain affects about 20% of total knee arthroplasty (TKA) patients, with high pain catastrophizing being a key predictor. Screening and addressing this modifiable factor may improve postoperative outcomes. Objective: We aimed to compare the effectiveness of two preoperative home-based multimodal physical therapy interventions on pain catastrophizing in high-catastrophizing TKA patients. Secondarily, the study aimed to assess postoperative outcomes over six months. Methods: A total of 40 patients with symptomatic osteoarthritis and moderate pain catastrophizing were randomly allocated to the control, therapeutic patient education (TPE), and multimodal physiotherapy (MPT) groups. Preoperative interventions comprised pain neuroscience education, coping skills training, and therapeutic exercise, differing in the number of sessions and degree of supervision. All outcomes were assessed before and after the treatment in the preoperative period, and 1, 3, and 6 months post-surgery. The primary outcome measure was pain catastrophizing. Results: Both intervention groups showed a preoperative reduction in pain catastrophizing. TPE patients had lower pain ratings at rest and lower catastrophizing scores at 1 and 6 months post-surgery, reduced kinesiophobia and improved dynamic balance at 3 and 6 months post-surgery, and higher self-efficacy at 1 month post-surgery. MPT patients exhibited lower pain catastrophizing and pain intensity during walking at 1 month post-surgery, and better outcomes in kinesiophobia, self-efficacy, and dynamic balance at 1, 3, and 6 months post-surgery, along with higher walking speed at 6 months post-surgery. Conclusions: Preoperative physiotherapy reduces preoperative pain catastrophizing and improves postoperative pain-related outcomes, behaviors, and cognitions in high-catastrophizing TKA patients. Registration is with the United States Clinical Trials Registry (NCT03847324).
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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 | 0.003 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".