Preoperative widespread pain sensitization and chronic pain after hip and knee replacement
Bibliographic record
Abstract
Chronic pain after joint replacement is common, affecting approximately 10% of patients after total hip replacement (THR) and 20% of patients after total knee replacement (TKR). Heightened generalized sensitivity to nociceptive input could be a risk factor for the development of this pain. The primary aim of this study was to investigate whether preoperative widespread pain sensitivity was associated with chronic pain after joint replacement. Data were analyzed from 254 patients receiving THR and 239 patients receiving TKR. Pain was assessed preoperatively and at 12 months after surgery using the Western Ontario and McMaster Universities Osteoarthritis Pain Scale. Preoperative widespread pain sensitivity was assessed through measurement of pressure pain thresholds (PPTs) at the forearm using an algometer. Statistical analysis was conducted using linear regression and linear mixed models, and adjustments were made for confounding variables. In both the THR and TKR cohort, lower PPTs (heightened widespread pain sensitivity) were significantly associated with higher preoperative pain severity. Lower PPTs were also significantly associated with higher pain severity at 12 months after surgery in the THR cohort. However, PPTs were not associated with the change in pain severity from preoperative to 12 months postoperative in either the TKR or THR cohort. These findings suggest that although preoperative widespread pressure pain sensitivity is associated with pain severity before and after joint replacement, it is not a predictor of the amount of pain relief that patients gain from joint replacement surgery, independent of preoperative pain severity.
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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 | 0.000 |
| 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) | 0.002 | 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".