Self‐defined former smokers consume the highest opioid quantities following knee and shoulder arthroscopy
Bibliographic record
Abstract
PURPOSE: To identify risk factors associated with increased postoperative opioid consumption and inferior pain outcomes following knee and shoulder arthroscopy. METHODS: Using the data set from the NonOpioid Prescriptions after Arthroscopic Surgery in Canada (NO PAin) trial, eight prognostic factors were chosen a priori to evaluate their effect on opioid consumption and patient-reported pain following arthroscopic knee and shoulder surgery. The primary outcome was the number of oral morphine equivalents (OMEs) consumed at 2 and 6 weeks postoperatively. The secondary outcome was patient-reported postoperative pain using the Visual Analogue Scale (VAS) at 2 and 6 weeks postoperatively. A multivariable linear regression was used to analyse these outcomes with eight prognostic factors as independent variables. RESULTS: Tobacco usage was significantly associated with higher opioid usage at 2 (p < 0.001) and 6 weeks (p = 0.02) postoperatively. Former tobacco users had a higher 2-week (p = 0.002) and cumulative OME (p = 0.002) consumption compared to current and nonsmokers. Patients with a higher number of comorbidities (p = 0.006) and those who were employed (p = 0.006) reported higher pain scores at 6 weeks. Patients in the 'not employed/other' category had significantly lower pain scores at 6 weeks postoperatively (p = 0.046). CONCLUSION: Former smoking status was significantly associated with increased post-operative opioid consumption following knee and shoulder arthroscopy at 2 and 6 weeks postoperatively. Increased pain was found to be significantly associated with employment status and an increasing number of comorbidities at 6 weeks postoperatively. These findings can aid clinicians in identifying and mitigating increased opioid utilization as well as worse pain outcomes in high-risk patient populations. LEVEL OF EVIDENCE: Level III, cohort study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".