Temporal trends of suicide-related non-traumatic out-of-hospital cardiac arrest characteristics and outcomes with the COVID-19 pandemic
Bibliographic record
Abstract
Background: Jurisdictions have reported COVID-19-related increases in the incidence and mortality of non-traumatic out-of-hospital cardiac arrest (OHCA). We hypothesized that changes in suicide incidence during the COVID-19 pandemic may have contributed to these changes. We investigated whether the COVID-19 pandemic was associated with changes in the: (1) incidence of suicide-related OHCA, and (2) characteristics and outcomes of such cases. Methods: communication of intent) one-year prior to, and one year after, the start of the COVID-19 pandemic (March 15, 2020). We calculated differences in incidence (with 95% CI), overall and within subgroups of mechanism (hanging, suffocation, poisoning, or unclear mechanism), and in case characteristics and hospital-discharge favourable neurological outcomes (CPC 1-2). Results: Of 13,785 EMS-assessed OHCA, we included 274/6430 (4.3%) pre-pandemic and 221/7355 (3.0%) pandemic-period suicide-related cases. The median age was 43 years (IQR 30-57), 157 (32%) were female, and 7 (1.4%) survived with favourable neurological status. Suicide-related OHCA incidence decreased from 5.4 pre-pandemic to 4.3 per 100 000 person-years (-1.1, 95% CI -2.0 to -0.28). Hanging-related OHCA incidence also decreased. Patient characteristics and hospital discharge outcomes between periods were similar. Conclusion: Suicide-related OHCA incidence decreased with the COVID-19 pandemic and we did not detect changes in patient characteristics or outcomes, suggesting that suicide is not a contributor to increases in COVID-related OHCA incidence or mortality. Overall suicide-related OHCA outcomes in both time periods were poor.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".