Abstract 290: The Association of Regional Intra-arrest Transport Practices for Out-of-hospital Cardiac Arrest with Survival and Neurological Status at Hospital Discharge
Bibliographic record
Abstract
Background: There is substantial regional variation in out-of-hospital cardiac arrest (OHCA) outcomes. We investigated whether regional-level intra-arrest transport practices were associated with patient outcomes. Methods: We performed a secondary analysis of the “CCC Trial” dataset, which included EMS-treated adult non-traumatic OHCA enrolled from 49 regional clusters. The exposure of interest was regional-level intra-arrest transport practices (RIATP), calculated as the proportion of cases within the enrolling cluster transported prior to return of spontaneous circulation (“intra-arrest transport”), divided into quartiles. We fit a multilevel mixed-effects logistic regression model to estimate the association of RIATP quartile and both survival and favorable neurologic status (mRS ≤ 3) at hospital discharge, adjusted for patient-level Utstein variables. Results: We included all 26,148 CCC-enrolled patients, 36% of whom were female, 97% were treated with prehospital ALS, and 23% had shockable initial rhythms. The median RIATP of the 49 clusters was 20% (IQR 6.2 - 30%). The figure shows outcomes stratified by RIATP quartile. Compared to the first quartile (<6.2%), increasing RIATP had the following adjusted associations with: (i) favourable neurological status: OR 0.87 (95% CI 0.60-1.26), 0.74 (95% CI 0.51-1.07), 0.36 (95% CI 0.25-0.53); and (ii) survival: 0.63 (95% CI 0.47-0.85), 0.60 (95% CI 0.45-0.79), 0.44 (95% CI 0.33-0.59). Conclusion: Treatment within a region that utilizes intra-arrest transport less frequently was associated with improved patient survival. These results may, in part, explain differences between regional OHCA survival outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".