Postoperative joint pain is associated with long-term all-cause mortality after total joint arthroplasty
Bibliographic record
Abstract
The aims of this study were to assess (1) if postoperative joint pain can predict long-term all-cause mortality after total joint arthroplasty (TJA), and (2) if postoperative joint pain was associated with causes of death (COD) in TJA patients. Patients who underwent total knee or hip arthroplasty were assessed once for their postoperative joint pain at least one-year after TJA using the Western Ontario and McMaster Universities Osteoarthritis Index Likert 3.0 pain subscale. Three pain definitions were utilized: "sustained pain" - pain on all five questions, "pain while active" - pain while walking and taking stairs, and "pain at rest" - pain while sitting/lying and at night while in bed. Patients reporting no pain were classified as controls. Associations between postoperative joint pain and mortality were assessed using Kaplan-Meier survival analysis and multivariable Cox proportional hazards regression to adjust for age at TJA, sex, body mass index (BMI), cardiovascular diseases (CVD), and cancer. The distribution of COD between pain groups and controls were compared using Fisher's exact test. A total of 727 patients were included in the study, of which 129 (18%) were deceased. The prevalence of postoperative sustained pain, pain while active, and pain at rest at 4-year after TJA was 10, 17, and 12%, respectively. The all-cause mortality rate at 11-year after TJA was 20, 26, 19, and 15% in these pain groups and controls, respectively, significantly higher in pain while active group (p = 0.006). Pain while active was positively associated with mortality when knee and hip patients were analyzed together and separately (p ≤ 0.03, hazard ratio (HR)≥1.80), and the significances became stronger after adjusting for age at surgery, sex, BMI, CVD, and cancer (p < 0.001, HR ≥ 2.57). No association was observed between postoperative joint pain and COD. Our results demonstrated that postoperative joint pain could be an important predictor for long-term all-cause mortality in TJA patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".