Indicators of publicly funded prescription opioid use among persons with traumatic spinal cord injury in Ontario, Canada
Bibliographic record
Abstract
To describe the proportion and identify predictors of community-dwelling individuals with traumatic spinal cord injury (TSCI) who were dispensed ≥1 publicly funded opioid in the year after injury using a retrospective cohort study. Ontario, Canada. We used administrative data to identify predictors of receiving publicly funded prescription opioids during the year after injury for individuals who were injured between April 2004 and March 2015. Our outcome was modeled using robust Poisson multivariable regression and we reported adjusted relative risks (aRR) with 95% confidence intervals. In our retrospective cohort of 934 individuals with TSCI who were eligible for the provincial drug program, 510 (55%) received ≥1 prescription opioid in the year after their injury. Most individuals were male (71%) and the median age was 63 years (interquartile range: 42–72). Being male (aRR 1.15, 95% confidence interval [CI] 1.01–1.31), having chronic obstructive pulmonary disease (aRR 1.25, 95% CI 1.05–1.50), and using prescription opioids before injury (aRR 1.46, 95% CI 1.29–1.66) were significantly associated with receiving opioids in the year after TSCI. Short durations of hospital stay after injury were also identified as being a significant risk factor of outpatient opioid use (aRR = 1.28, 95% CI = 1.08–1.51) when compared to longer hospital stays. This study presented evidence showing that most individuals eligible for Ontario’s public drug program who experienced a TSCI used opioids in the year following their injury. Due to the paucity of research on this population and their potential for elevated risks of adverse events, it is important for additional studies to be conducted on opioid use in this population to understand short-term and long-term risks and benefits.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".