Association between self-perceived pain sensitivity and pain intensity after cardiac surgery
Bibliographic record
Abstract
Background and purpose: Cardiac surgical pain remains a clinical challenge affecting about 40% of individuals in the first six months post-cardiac surgery, and continues up to two years after surgery for about 15–20%. Self-perceived sensitivity to pain may help to identify individuals at risk for persistent cardiac surgical pain to optimize health care responses. The purpose of this study was to assess the relationship between self-perceived pain sensitivity assessed by the Pain Sensitivity Questionnaire (PSQ) and postoperative worst pain intensity up to 12 months after cardiac surgery. Sex differences in baseline characteristics and the PSQ scores were also assessed. Methods: This study was performed among 416 individuals (23% women) scheduled for elective coronary artery bypass graft and/or valve surgery between March 2012 and September 2013. A secondary data-analysis was utilized to explore the relationship between preoperative PSQ scores and worst pain intensity rated preoperatively, across postoperative Days 1–4, at 2 weeks, and at 1, 3, 6, and 12 months post-surgery. Linear mixed model analyses were performed to estimate changes in pain intensity during 1-year follow-up. Results: The mean (±standard deviation) PSQ-total score was 3.3±1.4, with similar scores in men and women. The PSQ-total score was significantly associated with higher worst pain intensity ratings adjusted for participant characteristics ( p =0.001). Conclusion: Use of the PSQ before surgery may predict cardiac surgical pain intensity. However, previous evidence is limited and not consistent, and more research is needed to substantiate our results. Keywords: postoperative pain, acute pain, persistent pain, pain sensitivity, pain sensitivity questionnaire, cardiac 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".