Opioid use trends in patients undergoing elective thoracic and lumbar spine surgery
Bibliographic record
Abstract
Background: Opioid use in North America has increased rapidly in recent years. Preoperative opioid use is associated with several negative outcomes. Our objectives were to assess patterns of opioid use over time in Canadian patients who undergo spine surgery and to determine the effect of spine surgery on 1-year postoperative opioid use. Methods: A retrospective analysis was performed on prospectively collected data from the Canadian Spine Outcomes and Research Network for patients undergoing elective thoracic and lumbar surgery. Self-reported opioid use at baseline, before surgery and at 1 year after surgery was compared. Baseline opioid use was compared by age, sex, radiologic diagnosis and presenting complaint. All patients meeting eligibility criteria from 2008 to 2017 were included. Results: A total of 3134 patients provided baseline opioid use data. No significant change in the proportion of patients taking daily (range 32.3%-38.2%) or intermittent (range 13.7%-22.5%) opioids was found from pre-2014 to 2017. Among patients who waited more than 6 weeks for surgery, the frequency of opioid use did not differ significantly between the baseline and preoperative time points. Significantly more patients using opioids had a chief complaint of back pain or radiculopathy than neurogenic claudication (p < 0.001), and significantly more were under 65 years of age than aged 65 years or older (p < 0.001). Approximately 41% of patients on daily opioids at baseline remained so at 1 year after surgery. Conclusion: These data suggest that additional opioid reduction strategies are needed in the population of patients undergoing elective thoracic and lumbar spine surgery. Spine surgeons can be involved in identifying patients taking opioids preoperatively, emphasizing the risks of continued opioid use and referring patients to appropriate evidence-based treatment programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".