Impact of Perioperative Pain Intensity, Pain Qualities, and Opioid Use on Chronic Pain After Surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A better understanding of the pathogenesis of chronic postsurgical pain is needed in order to develop effective prevention and treatment interventions. The objective of this study was to evaluate the incidence and risk factors for chronic postsurgical pain in women undergoing gynecologic surgery. METHODS: Pain characteristics, opioid consumption, and psychologic factors were captured before and 6 months after surgery. Analyses included univariate statistics, relative risks (RRs) and 95% confidence intervals (95% CIs), and modified Poisson regression for binary data. RESULTS: Pain and pain interference 6 months after surgery was reported by 14% (n = 60/433) and 12% (n = 54/433), respectively. Chronic postsurgical pain was reported by 23% (n = 39/172) with preoperative pelvic pain, 17% (n = 9/54) with preoperative remote pain, and 5.1% (n = 10/197) with no preoperative pain. Preoperative state anxiety (RR = 1.8; 95% CI, 1.1-2.8), preoperative pain (pelvic RR = 3.7; 95% CI, 1.9-7.2; remote RR = 3.0; 95% CI, 1.3-6.9), and moderate/severe in-hospital pain (RR = 3.0; 95% CI, 1.0-9.4) independently predicted chronic postsurgical pain. The same 3 factors predicted pain-interference at 6 months. Participants describing preoperative pelvic pain as "miserable" and "shooting" were 2.8 (range, 1.3-6.4) and 2.1 (range, 1.1-4.0) times more likely to report chronic postsurgical pain, respectively. Women taking preoperative opioids were 2.0 (range, 1.2-3.3) times more likely to report chronic postsurgical pain than those not taking opioids. Women with preoperative pelvic pain who took preoperative opioids were 30% (RR = 1.3; 95% CI, 0.8-1.9) more likely to report chronic postsurgical pain than those with preoperative pelvic pain not taking opioids. CONCLUSIONS: Preoperative pain, state anxiety, pain quality descriptors, opioid consumption, and early postoperative pain may be important predictors of chronic postsurgical pain, which require further investigation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".