Reporting Rates of Opioid-Related Adverse Events Since 1965 in Canada: A Descriptive Retrospective Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patients with chronic or acute/postoperative pain frequently use opioids. However, opioids may cause considerable adverse reactions (ARs), such as respiratory depression, which could be lethal. Unfortunately, only 5% of drug-related ARs (including those to opioids) are reported to health authorities. Therefore, little is known regarding the occurrence of opioid-related ARs at the population level. OBJECTIVE: The aim of this study was to investigate how the rates of reported opioid-related ARs have changed in Canada since 1965. METHODS: Our retrospective study examined trends of reported opioid-related ARs occurring in hospitalized and outpatients. Data on opioid-related ARs and mortality between 1965 and 2019 were obtained from the Canada Vigilance and Statistics Canada databases. Descriptive and Joinpoint regression analyses were performed. RESULTS: Oxycodone and normethadone were the most and least involved opioid agents, respectively, among the 18,407 reported ARs. The highest rate of reported opioid ARs (3.8 per 100,000 person-years) was recorded in 2012, whereas the lowest was recorded in 1965 (0.1 per 100,000 person-years). Between 1965 and 2019, annual rates climbed by 4.2% (95% confidence interval [CI] 3.1-5.2), and many fluctuations were observed: 1965-1974: +22.3% (95% CI 12.0-33.6); 1974-2000: - 4.1% (95% CI - 5.3 to - 2.9); 2000-2008: +30.3% (95% CI 22.6-38.4); 2008-2014: +4.1% (95% CI - 1.5 to 10.1); 2014-2017: -26.0% (95% CI - 44.7 to - 0.9); and, finally, 2017-2019: +35.4% (95% CI 3.8-76.7). CONCLUSION: Reported opioid-related ARs have increased since 1965, although fluctuations were observed in recent decades. The absolute number of opioid-related ARs might be seriously underestimated. Future studies should look into how to close this gap.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it