Smoking, Pain Intensity, and Opioid Consumption 1–3 Months After Major Surgery: A Retrospective Study in a Hospital-Based Transitional Pain Service
Bibliographic record
Abstract
Introduction: The present study investigated the associations between smoking, pain, and opioid consumption in the 3 months after major surgery in patients seen by the Transitional Pain Service. Current smoking status and lifetime pack-years were expected to be related to higher pain intensity, more opioid use, and poorer opioid weaning after surgery. Methods: A total of 239 patients reported smoking status in their presurgical assessment (62 smokers, 92 past smokers, and 85 never smokers). Pain and daily opioid use were assessed in hospital before postsurgical discharge, at first outpatient visit (median of 1 month postsurgery), and at last outpatient visit (median of 3 months postsurgery). Pain was measured using numeric rating scale. Morphine equivalent daily opioid doses were calculated for each patient. Results: Current smokers reported significantly higher pain intensity (p < .05) at 1 month postsurgery than never smokers and past smokers. Decline in opioid consumption differed significantly by smoking status, with both current and past smokers reporting a less than expected decline in daily opioid consumption (p < .05) at 3 months. Decline in opioid consumption was also related to pack-years, with those reporting higher pack-years having a less than expected decline in daily opioid consumption at 3 months (p < .05). Conclusions: Smoking status may be an important modifiable risk factor for pain intensity and opioid use after surgery. Implications: In a population with complex postsurgical pain, smoking was associated with greater pain intensity at 1 month after major surgery and less opioid weaning 3 months after surgery. Smoking may be an important modifiable risk factor for pain intensity and opioid use after 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".