Activity-pacing and outcomes of total knee arthroplasty: A longitudinal study
Bibliographic record
Abstract
Background: Psychological factors may induce chronic pain and lead to inactivity after total knee arthroplasty (TKA). The impact of excessive variations in physical activity on psychological factors remains unclear. Aims/Objectives: This study investigated the impact of wide variations in physical activity during occupational therapy (OT) in the early period after TKA. Materials and Methods: We enrolled 30 TKA patients. Activities were measured postoperatively for 1 week. Patients were assigned to “good-pacing” or “poor-pacing” groups based on the correlation between physical activity and OT day. The outcome indices were Canadian occupational performance measure, pain (resting and walking), pain catastrophizing (rumination, helplessness, and magnification), anxiety, depression, and pain self-efficacy. Results: Twenty (66.6%) patients demonstrated good pacing, while ten (33.3%) showed poor pacing. The good-pacing group showed increased physical activity as the OT day increased. On the contrary, physical activity did not increase with OT day in the poor-pacing group, and these patients exhibited significantly higher walking pain, anxiety, and depression than those in the good-pacing group (p < 0.05). Conclusion: TKA patients with excessive variation in physical activity during OT demonstrated higher pain, anxiety, and depression. Significance: Physical activity variations could improve the postoperative outcomes of TKA patients.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".