ASSESSMENT OF CHANGES IN OPIOID UTILIZATION ONE YEAR AFTER ELECTIVE SPINE SURGERY: A CANADIAN SPINE OUTCOMES AND RESEARCH NETWORK STUDY
Bibliographic record
Abstract
Chronic opioid use has been associated with negative individual and societal consequences. Our primary objective was to identify perioperative changes in opioid use amongst patients undergoing elective spinal surgery. Our secondary objective was to assess baseline and perioperative factors associated with positive or negative change in opioid use at one-year post surgery. A retrospective review of CSORN data (patient reported measures, socio-demographic factors, lifestyle factors and perioperative procedural information) was performed. Multivariable logistic regression models were used to examine the associations between these factors and change in opioid use. Our data included 5059 patients (2628 (51.9%)-non-users/2431(48.1%)-users) with opioid change data, of which 52.7% were male. At one-year postoperative, 77.5% of patients were not using opioids. Patients were stratified into the following subgroups based on change in baseline opioid use status: (a) non-users-no-change (47.4%), (b) users-no-change (17.9%), (c) non-user-changed to users (4.5%), and (d) user-changed to non-user (30.1%). In other words, 62.7% of baseline users became non-users and 8.7% of non-users became users at one-year. The following baseline factors were independently associated with 1) opioid users who became non-users: lower BMI, fewer comorbidities, non-smoker, not living alone, no insurance claims, routinely exercising, shorter operations, spondylolisthesis diagnosis; 2) opioid users who remained users: higher BMI, more comorbidities, cervical spine location, smoking, married, living alone, compensation claims, not working, no routine exercise, higher PHQ9 score, longer operating time, no spondylolisthesis; and 3) non-users who became users: more comorbidities, longer symptom duration, higher PHQ9 score, and longer LOS, no spondylolisthesis diagnosis. The majority of spine surgery patients are not taking opioids at one-year postoperatively. This includes two-thirds of those that were taking opioids prior to surgery. Further study of the 1-in-5 patients who are persistent or new users at follow-up is required in order to identify possible modifiable baseline risk factors and develop targeted mitigation strategies in the perioperative period.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".