Persistent post‐operative opioid use following hip arthroscopy is common and is associated with pre‐operative opioid use and age
Bibliographic record
Abstract
PURPOSE: Hip arthroscopy utilization continues to increase worldwide. Post-operative pain management is essential to allow appropriate rehabilitation. While multimodal analgesic protocols have been described, consensus agreement is lacking and opioid analgesia remains a mainstay of treatment. Unfortunately, the risk of persistent opioid use among opioid-naïve and non-naïve patients following hip arthroscopy remains unclear. Therefore, the purpose of this study was to identify rates of persistent post-operative opioid use, as well as to identify factors associated with persistent use. METHODS: A retrospective cohort study was conducted using linked administrative data from Ontario, Canada. Participants were adults who underwent hip arthroscopy between 2013 and 2018. Patients < 18 or > 60 years of age as well as those who had undergone prior hip arthroscopy were excluded. The primary exposure was whether patients had filled ≥ 2 opioid prescriptions within 1 year prior to their hip arthroscopy to define the opioid naïve and non-naïve populations. The primary outcome was persistent opioid use, defined as 2 + prescriptions filled between 9 and 15 months post-op. A regression analysis was performed to identify factors associated with persistent opioid usage. RESULTS: Of the 1909 patients, 1525 (79.9%) were opioid-naïve, while 384 (20.1%) had a prior history of opioid use within 1 year of surgery. 224 patients (11.7%) demonstrated persistent opioid use, with ≥ 2 prescriptions filled between 9 and 15 months post-op. Of those, 42 (18.8%) cases were among opioid-naïve patients, while the remaining 182 (81.2%) were among non-naïve patients. The risk of persistent post-operative use was significantly higher in those with prior opioid use (OR 31.95, 95% CI 22.15-46.09; p < 0.0001). Regression analysis confirmed that pre-operative opioid use (OR 23.79, 95% CI 17.06-33.17; p < 0.0001) and older age (OR 1.04, 95% CI 1.02-1.05, p < 0.0001) were associated with increased risk of persistent post-operative opioid use. CONCLUSION: Following hip arthroscopy, persistent opioid use is common. New persistent use was identified in 2.7% of opioid-naïve patients, compared with continued use in 47.4% of non-naïve patients. Pre-operative opioid use and older age were associated with the greater risk of persistent post-operative opioid use. LEVEL OF EVIDENCE: Level III.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".