Geographical Variation in Opioid Prescribing and Opioid-Related Mortality in Ontario
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
The Issue Overdoses and deaths involving prescription opioids are a major public health concern. Recent data from the United States indicate an opioid-related death rate of 6.4 per 100,000 population annually, which exceeds the annual human immunodeficiency virus–related death rate at 4.0 per 100,000 population (Centers for Disease Control and Prevention 2009; Heron et al. 2009). Although the relationship between opioid prescriptions and the risk of adverse events is becoming more widely appreciated (Dhalla et al. 2009; Dunn et al. 2010), opioid prescribing practices, abuse and diversion have been shown to exhibit substantial geographical variability, and this may have implications for public health policy decisions and interventions (Curtis et al. 2006; Webster et al. 2009).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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