Can Extended-release Injectable Medications Help Curb United States and Canada's Opioid Overdose Epidemic?
Bibliographic record
Abstract
Settings throughout the United States and Canada continue to face escalating overdose epidemics. Notably, history of overdose is associated with increased risk of fatal overdose. Unfortunately, despite frequent contact with health services and the well-known mortality benefits of medications for opioid use disorder (MOUD), only a fraction of overdose survivors is successfully linked to addiction care after leaving the emergency department. This may be partially explained by well-documented challenges of oral MOUD, including the need for frequent visits to the pharmacy to receive their medications, which may limit the flexibility to acquire or sustain employment, and therefore contribute to high rates of opioid addiction care discontinuation. This commentary discusses the potential fit of different extended-release injectable MOUD to circumvent limitations of oral formulations, and thereby improve linkage and retention in care of high-risk populations, such as opioid-overdose survivors.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".