Out-of-hospital cardiac arrest in the Algarve region of Portugal: a retrospective registry trial with outcome data.
Bibliographic record
Abstract
BACKGROUND AND IMPORTANCE: Out-of-hospital cardiac arrest is a leading cause of death in Europe. An understanding of region-specific factors is essential for informing strategies to improve survival. DESIGN: This retrospective observational study included all out-of-hospital cardiac arrest patients attended by the Emergency Medical Service of the Algarve in 2019. Outcome data were derived from hospital records. MAIN RESULTS: In 2019, there were 850 out-of-hospital cardiac arrests treated with cardiopulmonary resuscitation in the Algarve, representing a population incidence of 189/100 000. Return of spontaneous circulation occurred in 83 patients (9.8%), of whom 17 (2.0%) had survival to hospital discharge and 15 (1.8%) had survival with good neurologic outcome. Among patients in the Utstein comparator group, survival to hospital discharge was 21.4%. Predictors of return of spontaneous circulation were age, witnessed arrest, initial shockable rhythm, time of year, time to cardiopulmonary resuscitation, and time to advanced life support. Predictors of survival to hospital discharge were age, initial shockable rhythm, time to rhythm analysis, and time to advanced life support. Predictors of survival with good neurologic outcome were age, initial shockable rhythm, and time to return of spontaneous circulation. CONCLUSIONS: The incidence of out-of-hospital cardiac arrest with cardiopulmonary resuscitation in the Algarve was higher than in other jurisdictions while return of spontaneous circulation, survival to hospital discharge, and survival with good neurologic outcome were comparatively low. An aging population, a geographically diverse region, and a low incidence of bystander cardiopulmonary resuscitation may have contributed to these outcomes. These results confirm the importance of early cardiopulmonary resuscitation, early rhythm assessment, and early advanced life support, all of which are potentially modifiable through public education, broadening of the defibrillator network and increased availability of advanced life support teams.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".