Incidence and Risk Factors of Long-term Opioid Use in Elderly Trauma Patients
Bibliographic record
Abstract
OBJECTIVE: Evaluate the incidence and risk factors of opioid use 1 year after injury in elderly trauma patients. BACKGROUND: The current epidemic of prescription opioid misuse and overdose observed in North America generally concerns young patients. Little is known on long-term opioid use among the elderly trauma population. METHODS: In a retrospective observational multicenter cohort study conducted on registry data, all patients 65 years and older admitted (hospital stay >2 days) for injury in 57 adult trauma centers in the province of Quebec (Canada) between 2004 and 2014 were included. We searched for filled opioid prescriptions in the year preceding the injury, up to 3 months and 1 year after the injury. RESULTS: In all, 39,833 patients were selected for analysis. Mean age was 79.3 years (±7.7), 69% were women, and 87% of the sample was opioid-naive. After the injury, 38% of the patients filled an opioid prescription within 3 months and 10.9% [95% confidence interval (CI) 10.6%-11.2%] filled an opioid prescription 1 year after trauma: 6.8% (95% CI 6.5%-7.1%) were opioid-naïve and 37.6% (95% CI 36.3%-38.9%) were opioid non-naive patients. Controlling for confounders, patients who filled 2 or more opioid prescriptions before the injury and those who filled an opioid prescription within 3 months after the injury were, respectively, 11.4 and 3 times more likely to use opioids 1 year after the injury compared with those who did not fill opioid prescriptions. CONCLUSIONS: These results highlight that elderly trauma patients are at risk of long-term opioid use, especially if they had preinjury or early postinjury opioid consumption.
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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 | 0.001 | 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) | 0.001 | 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".