The association of non-prescription drug use preceding out-of-hospital cardiac arrest and clinical outcomes
Bibliographic record
Abstract
BACKGROUND: Clinicians may make prognostication decisions for out-of-hospital cardiac arrest (OHCA) using historical details pertaining to non-prescription drug use. However, differences in outcomes between OHCAs with evidence of non-prescription drug use, compared to other OHCAs, have not been well described. METHODS: We included emergency medical service-treated OHCA in the British Columbia Cardiac Arrest Registry (January/2019-June/2023). We classified cases as "non-prescription drug-associated cardiac arrests" (DA-OHCA) if there was evidence of non-prescription drug use preceding the OHCA, including witness accounts of use within 24 h or paraphernalia at the scene. We fit logistic regression models to investigate the association between DA-OHCA (vs. other cases) and favourable neurological outcome (Cerebral Performance Category [CPC] 1-2) and survival at hospital discharge, and return of spontaneous circulation (ROSC). RESULTS: Of 18,426 OHCA, 2,171 (12%) were classified as DA-OHCA. DA-OHCA tended to be younger, unwitnessed, occur during the evening or night, and present with a non-shockable rhythm, compared to other OHCA. DA-OHCA (221 [10%]) had a greater proportion (difference 1.8%; 95% CI 0.49-3.2) with favourable neurological outcomes compared to other OHCA (1,365 [8.4%]). Adjusted models did not identify an association of DA-OHCA with favourable neurological outcome (OR 1.08, 95% CI 0.87-1.33) or survival to hospital discharge (OR 1.13, 95% CI 0.93-1.38), but did demonstrate an association with ROSC (OR 1.13, 95% CI 1.004-1.27). CONCLUSION: In unadjusted models, DA-OHCA was associated with an improved odds of survival and favourable neurological outcomes at hospital discharge, compared to other OHCA. However, we did not detect an association in adjusted analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".