Registries to measure and improve outcomes after cardiac arrest
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiac arrest registries are used to measure and improve the process and outcome of resuscitation care, and can give insight into risk factors, prognosis, and the effectiveness of interventions to mitigate its impact. This review provides an overview of current out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA) registries, with attention to key recent findings and future directions. RECENT FINDINGS: Major OHCA registries include the Resuscitation Outcomes Consortium Cardiac Arrest Epistry and Cardiac Arrest Registry to Enhance Survival. Registry data from IHCA largely stem from the US and Canada with Get with the Guidelines-Resuscitation, and the UK with the National Cardiac Arrest Audit. Each registry has strengths and limitations. Important findings include trends in survival, racial disparities in care, and hospital and community-level variations in performance, as well as estimates of the effectiveness of individual interventions. Utstein definitions facilitate uniform reporting of the process and outcome of care, and are currently being updated. Standardization of registry data is an ongoing challenge. SUMMARY: OHCA and IHCA registries are invaluable in advancing our understanding of resuscitation care, as well as variations in international practice. Investigations that compare and contrast outcomes from established and evolving registries will help advance resuscitation science further.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".