Abstract 246: Incidence and Outcomes of Rearrest Following Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Rearrest (RA) occurs when a patient experiences cardiac arrest after successfully achieving return of spontaneous circulation (ROSC). The incidence and outcomes of RA following out-of-hospital cardiac arrest (OHCA) have been estimated in limited local studies. We sought to investigate the incidence and outcomes of RA over a broad geographic area. Methods: This retrospective study was approved by the University of Pittsburgh Institutional Review Board. We obtained case data from EMS-treated, non-traumatic OHCA from the Resuscitation Outcomes Consortium, a multi-site clinical research network conducting population level surveillance of OHCA in 11 cities in the US and Canada. The study cohort comprised all OHCA cases surveilled between 2006 and 2008 at ROC sites and having prehospital ROSC. We used three methods to ascertain RA incidence among these cases: direct signal analysis, indirect cardiopulmonary resuscitation (CPR) process analysis, and emergency department arrival vital status. RA incidence was estimated as the proportion of cases with ROSC that experience RA. Regional RA estimates were compared with the Chi-Squared test. Multivariable logistic regression was used to assess the relationship between RA and survival to hospital discharge. Results: Out of 18,937 cases of EMS-assessed OHCA captured between 2006 and 2008, 11,456 (60.5%) cases were treated by EMS and 4,396 (38.4%) had prehospital ROSC. Of these, sufficient data were available for RA ascertainment in 3,253 cases, with 568 (17.5%) experiencing RA. Regional RA incidence varied significantly from 10.2% to 21.2% (p < 0.001). RA was significantly inversely associated with survival (OR: 0.19, 95%CI: 0.14 - 0.26). Conclusions: In this geographically broad and inferentially conservative analysis, RA occurred on average in 1 of every 6 successfully resuscitated patients, though incidence varied significantly across 10 sites in North America. RA was found to be inversely associated with survival to hospital discharge.
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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 | 0.000 |
| 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".