Risk of Injury Associated with Opioid Use in Older Adults
Bibliographic record
Abstract
OBJECTIVES: To estimate the dose-related risk of injuries in older adults associated with the use of low-, medium-, and high-potency opioids. DESIGN: Historical population-based cohort study: 2001 to 2003. SETTING: Quebec, Canada's, universal healthcare system. PARTICIPANTS: Four hundred three thousand three hundred thirty-nine adults aged 65 and older. MEASUREMENTS: Population-based health databases were used to measure preexisting risk factors for injuries in 2001/02 and drug use and injuries during follow-up (2003). Type and dose of opioids were measured as time-dependent variables, as were other drugs that may increase the risk of injury from sedating side-effects or hypotension. The risk of injury per one adult dose increase in opioid dose was estimated using multivariate Cox proportional hazards models. RESULTS: During the follow-up year, 50.7% of the study population were prescribed drugs with sedating side effects, 15.3% were prescribed an opioid, 20.7% were concurrently using more than one sedating medication, and 3.7% were treated for an injury, fractures (55.1%) being the most common. After adjusting for concurrent drug use and baseline risk factors, low- (hazard ratio (HR)=1.36, 95% confidence interval (CI)=1.33-1.39) and intermediate-potency (HR=1.05, 95% CI=1.02-1.07) opioids were associated with the risk of injury. Use of codeine combinations was associated with the highest risk of injury, a 127% greater risk (HR=2.27, 95% CI=2.21-2.34) per one adult dose increase. (The mean World Health Organization standardized dose in the study population was 1.71 ± 0.85 adult doses.) CONCLUSION: Opioids increase the risk of injury in older adults, particularly codeine combinations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".