Rationale, methods, and progress of the ArthroCaP Study: A prospective cohort study exploring the associations between chronic postsurgical pain and postoperative cognitive dysfunction after elective knee or hip arthroplasty
Bibliographic record
Abstract
Chronic postsurgical pain (CPSP) may increase the risk of postoperative cognitive dysfunction (POCD) directly or by negatively impacting mobility. A comprehensive measure of satisfaction with surgery that accounts for ability to perform activities might be even more strongly associated with POCD. There might be complex mechanisms underlying the interplays between postoperative pain and cognition. The primary objective is to explore whether CPSP at 6 months is associated with POCD (≥2-point decline in the Montreal Cognitive Assessment [MoCA] compared to preoperative) at 12 months. Secondary objectives are to explore the association between satisfaction with surgery and POCD, the role of pain medications (opioids) in the association between CPSP and POCD, the role of patient preoccupation/optimism and coping in determining the effect of pain on cognition, and the hypothesis of neurogenesis interference as an underlying mechanism. We will prospectively recruit ≥100 patients ≥50 years old undergoing elective total knee/hip arthroplasty. Sociodemographic characteristics, comorbidities, frailty, pain, and pain medications will be assessed preoperatively. The Somatic Preoccupation and Coping questionnaire will be administered preoperatively and 4 to 6 weeks postoperative. Pain and the Self-Administered Patient Satisfaction Scale will be measured at 3 and 6 months. MoCA and neuropsychological tests will be administered at baseline, 4 to 6 weeks, and 6 and 12 months. Blood will be longitudinally collected for biomarker analysis. Forty-six participants have been enrolled in the study so far. ArthroCaP will provide preliminary data on the association of CPSP and patient satisfaction with POCD and underlying mechanisms. It will inform larger confirmatory and interventional studies to enhance the benefits of surgery.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".