Occupational Therapy Using Coping Lists After Total Knee Arthroplasty: A Case Series
Bibliographic record
Abstract
Total knee arthroplasty (TKA) can improve the postoperative quality of life in patients with severe knee osteoarthritis. Although occupational therapy (OT) using a coping list may be useful for post-TKA patients, its use has not been documented. This study aimed to explore the effectiveness of OT using coping skills. Five post-TKA patients underwent OT using coping skills. The Canadian Occupational Performance Measure (COPM), numerical rating scale (NRS), Hospital Anxiety and Depression Scale (HADS), EQ-5D (EuroQol-5-dimension)-5-level (5L), EQ-5D Visual Analogue Scale (VAS), modified fall efficacy scale (MFES), Pain Disability Assessment Scale (PDAS), and coping skills were measured at the start and end of the study. Significant improvements were observed in COPM, NRS, HADS, EQ-5D-5L, and PDAS scores (p <0.05). No significant improvements were found in the EQ-5D VAS and MFES scores. All evaluations showed a large effect size (r ≤ 0.5). The total number of coping skills also increased. This report suggests that OT with coping strategies is effective for pain, psychological factors, quality of life, and activities of daily living. Incorporating coping skills in OT may be useful in postoperative TKA pain management. However, larger studies are needed to validate this.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".