The influence of preoperative psychological distress on pain and function after total knee arthroplasty
Bibliographic record
Abstract
AIMS: We performed a meta-analysis investigating the association between preoperative psychological distress and postoperative pain and function after total knee arthroplasty (TKA). MATERIALS AND METHODS: Pubmed/Medline, Embase, PsycINFO, and the Cochrane library were searched for studies on the influence of preoperative psychological distress on postoperative pain and physical function after TKA. Two blinded reviewers screened for eligibility and assessed the risk of bias and the quality of evidence. We used random effects models to pool data for the meta-analysis. RESULTS: Six prospective cohort studies, with a total of 1525 patients, were included. The random effects models showed significantly poorer outcomes in patients who preoperatively had elevated scores on the pain catastrophizing scale, worse 36-Item Short-Form Health Survey (SF-36) mental health score, symptoms of anxiety and/or depression, and somatization dysfunction. After 12 months, the standard mean difference for pain was -0.74 (95% confidence interval (CI) -1.04 to -0.44) and -0.56 (95% CI -0.80 to -0.32) for function. CONCLUSION: Preoperative pain catastrophizing, mental distress, symptoms of anxiety and/or depression, and somatoform disorders appear to adversely affect pain and function after TKA. Some patients undergoing TKA may therefore need psychological support to improve the outcome and quality of life.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".