Canadian Guidelines on Opioid Use Disorder Among Older Adults
Bibliographic record
Abstract
BACKGROUND: In Canada, rates of hospital admission from opioid overdose are higher for older adults (≥ 65) than younger adults, and opioid use disorder (OUD) is a growing concern. In response, Health Canada commissioned the Canadian Coalition of Seniors' Mental Health to create guidelines for the prevention, screening, assessment, and treatment of OUD in older adults. METHODS: A systematic review of English language literature from 2008-2018 regarding OUD in adults was conducted. Previously published guidelines were evaluated using AGREE II, and key guidelines updated using ADAPTE method, by drawing on current literature. Recommendations were created and assessed using the GRADE method. RESULTS: Thirty-two recommendations were created. Prevention recommendations: it is key to prioritize non-pharmacological and non-opioid strategies to treat acute and chronic noncancer pain. Assessment recommendations: a comprehensive assessment is important to help discern contributions of other medical conditions. Treatment recommendations: buprenorphine is first line for both withdrawal management and maintenance therapy, while methadone, slow-release oral morphine, or naltrexone can be used as alternatives under certain circumstances; non-pharmacological treatments should be offered as an integrated part of care. CONCLUSION: These guidelines provide practical and timely clinical recommendations on the prevention, assessment, and treatment of OUD in older adults within the Canadian context.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".