Opioid poisoning and availability of specialized medical care in Ontario, 2002-2006
Bibliographic record
Abstract
The prescription of opioid analgesics has risen sharply in North America over the past \ntwo decades. This increase has been accompanied by a rise in overdoses. The \npresent study draws on administrative data collected from emergency department \ncontacts to describe the epidemiology of opioid overdose in Ontario b~tween 2002 \nand 2006 and to examine the role of regional variation in availability of specialist \ncare. \nThe number of poisonings increased from 1250 (10.9 per 100,000) in FY2002 to \n1816 (15.2 per 100,000) in FY2005. Local concentration of specialist physicians was \nsignificantly associated with the incidence of opioid overdose, inversely at most \nlevels of availability, but positively at very high levels. Regional variation in \nincidence was also associated with demographics, median family income, and the rate \nof other drug poisonings. Policy options for limiting opioid-related harms are limited, \nbut improvements in monitoring and clinical management may prove valuable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".