Increased risk of new persistent opioid use in pediatric and young adult patients with kidney stones
Bibliographic record
Abstract
INTRODUCTION: Adolescents and young adults are a vulnerable patient population for development of substance use disorder. However, the long-term impact of opioid prescribing in young adult patients with renal colic is not known. Our objective was to describe rates of opioid prescription and identify risk factors for persistent opioid use in patients age 25 years or younger with renal colic from kidney stones. METHODS: Using previously validated, linked administrative databases, we performed a population-based, retrospective cohort study of opioid-naive patients age 25 years or younger with renal colic between July 1, 2013 and September 30, 2017 in Ontario. All family practitioner, urgent care, and specialist visits in the province were captured. Our primary outcome was persistent opioid use, defined as filling a prescription for an opioid between 91 and 180 days after initial visit. Ontario uses a narcotic monitoring system, which captures all opioids dispensed in the province. RESULTS: Of the 6962 patients identified, 56% were prescribed an opioid at presentation and 34% of those were dispensed more than 200 oral morphine equivalents. There was persistent opioid use in 313 (8.1%) patients who filled an initial opioid prescription. In adjusted analysis, those prescribed an opioid initially had a significantly higher risk of persistent opioid use (odds ratio [OR] 1.85; 95% confidence interval [CI] 1.50-2.29) and opioid overdose (OR 3.45; 1.08-11.04). There was a dose-dependent increase in risk of persistent opioid use with escalating initial opioid dose. History of mental illness (OR 1.32; 1.02-1.71) and need for surgery (OR 1.71; 1.24-2.34) were also associated with persistent opioid use. CONCLUSIONS: Among patients with kidney stones age 25 years or younger, filling an opioid prescription after presentation is associated with an increased risk of persistent opioid use 3-6 months later and a higher risk of serious long-term complications, such as opioid overdose.
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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 | 0.001 |
| 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".