Influence of increased pain sensitivity on patient‐reported outcomes following total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to discover whether increased pain sensitivity was associated with postoperative pain and patient-reported outcome measures (PROMs) after total knee arthroplasty (TKA). METHODS: Pain sensitivity was evaluated preoperatively using a pain sensitivity questionnaire (PSQ). Resting, walking, nighttime, and average pain visual analog scale (VAS) were measured before surgery and 6 weeks, 3 months, 6 months, and 1 year after surgery. PROMs were also evaluated based on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and patient satisfaction. The association between pain VAS average score, WOMAC total score, and PSQ score (minor, moderate, and total score) was assessed at each stage. RESULTS: There were 59 patients with a high PSQ score (≥ 5.2) and 53 with a low PSQ score (< 5.2). Up to 1 year postoperatively, the group with high PSQ scores had higher resting, walking, nighttime, and average pain VAS scores than the group with low scores (all p < 0.05). Worse preoperative WOMAC pain, function, and total scores continued until 1 year after surgery in the high-scoring PSQ group (all p < 0.05). The group with low PSQ scores was more satisfied with surgery than the group with high scores (p = 0.027). There was a positive correlation between preoperative PSQ score and pain VAS average score at all time points (all p < 0.05). A relationship between PSQ score and WOMAC total score was also observed (all p < 0.05). CONCLUSION: Increased pain sensitivity is a factor related to higher postoperative pain levels and inferior PROMs in patients undergoing primary TKA. LEVEL OF EVIDENCE: Case-controlled study, III.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".