Abstract 236: Regional Incidence and Outcome of Out-of-Hospital Cardiac Arrest Associated with Overdose
Bibliographic record
Abstract
Introduction: The frequency of lethal overdose due to prescription and non-prescription drugs is increasing in North America. The contribution of drug overdose (OD) to regional variation in the incidence and outcome out-of-hospital cardiac arrest (OHCA) is unclear. Objective: To estimate overall and regional variation in incidence and outcomes of emergency medical services (EMS)-treated OD-OHCA cases across North America. Methods: The Resuscitation Outcomes Consortium (ROC) is a clinical research network with 10 regional clinical centers in United States (US) and Canada that uses uniform methods for surveillance of all EMS-treated OHCA in participating regions. Cases of OHCA from 2006 to 2010 were reviewed for evidence of association with or without OD. Incidence of OD-OHCA was calculated as the number of OD-OHCA in a region per 100,000 cumulative person-years, using 2000 US Census and 2006 Statistics Canada population counts. Patient and EMS characteristics as well as outcome were described. Multiple logistic regression was used to describe the association between OD status on return of spontaneous circulation (ROSC) and survival to hospital discharge, while adjusting for case characteristics and consortium center. Results: Included were 56,272 cases of OHCA. Regional incidence of OD-OHCA varied between 0.5 and 2.7 per 100,000 person years (p<0.001), and proportion of OD-OHCA among all EMS-treated OHCA ranged from 0.9% to 3.8%. Table 1 shows outcomes and characteristics stratified by OD status; OD-OHCA were younger, less likely to be witnessed, and less likely to present with a shockable rhythm. Compared to non-OD, OD-OHCA was associated with ROSC (OR: 1.55; 95%CI: 1.35-1.78) and survival (OR: 2.14; 95%CI: 1.72-2.65). Conclusions: OD-OHCA are a small proportion of all OHCA, although incidence varied up to 5-fold across regions. OD-OHCA were more likely to survive than non-OD-OHCA.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".