Suspected Heroin Overdoses in US Emergency Departments, 2017–2018
Bibliographic record
Abstract
Objectives. To describe changes in suspected heroin overdose emergency department (ED) visits. Methods. We analyzed quarterly and yearly changes in heroin overdoses during 2017–2018 by using data from 23 states and jurisdictions (including the District of Columbia) funded by the Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance program. The analyses included the Pearson χ 2 test to detect significant changes. Results. Both sexes, all age groups, and some states exhibited increases from quarter 1 (Q1) 2017 to Q2 2017 and significant decreases in both quarters from Q3 2017 to Q1 2018 in heroin overdose ED visits. Overall, there was a significant yearly decline of 21.5% in heroin overdose ED visits. Three states had significant yearly increases (Illinois, Indiana, and Utah), and 9 states (Kentucky, Maryland, Massachusetts, New Hampshire, Ohio, Pennsylvania, Rhode Island, West Virginia, and Wisconsin) and the District of Columbia had significant decreases. Conclusions. We identified decreases in heroin overdose ED visits from 2017 through 2018, but these declines were not consistent among states. Even with the possibility of a stabilization or slowing of this epidemic, it is important that the field of public health and its partners implement strategies to prevent overdoses and target emerging hot spots.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".