Predictors of Persistent Post-Surgical Pain Following Total Knee Arthroplasty: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
OBJECTIVE: Approximately one in four total knee replacement patients develop persistent pain. Identification of those at higher risk could help inform optimal management. METHODS: We searched MEDLINE, EMBASE, CINAHL, AMED, SPORTDiscus, and PsycINFO for observational studies that explored the association between risk factors and persistent pain (≥3 months) after total knee replacement. We pooled estimates of association for all independent variables reported by >1 study. RESULTS: Thirty studies (26,517 patients) reported the association of 151 independent variables with persistent pain after knee replacement. High certainty evidence demonstrated an increased risk of persistent pain with pain catastrophizing (absolute risk increase [ARI] 23%, 95% confidence interval [CI] 12 to 35), younger age (ARI for every 10-year decrement from age 80, 4%, 95% CI 2 to 6), and moderate-to-severe acute post-operative pain (ARI 30%, 95% CI 20 to 39). Moderate certainty evidence suggested an association with female sex (ARI 7%, 95% CI 3 to 11) and higher pre-operative pain (ARI 35%, 95% CI 7 to 58). Studies did not adjust for both peri-operative pain severity and pain catastrophizing, which are unlikely to be independent. High to moderate certainty evidence demonstrated no association with pre-operative range of motion, body mass index, bilateral or unilateral knee replacement, and American Society of Anesthesiologists score. CONCLUSIONS: Rigorously conducted observational studies are required to establish the relative importance of higher levels of peri-operative pain and pain catastrophizing with persistent pain after knee replacement surgery.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".