Survival after in-hospital cardiopulmonary resuscitation from 2003 to 2013
Bibliographic record
Abstract
We analyzed cardiopulmonary resuscitation (CPR) rates, deaths preceded by CPR, and survival trends after in-hospital CPR, using a sample of nationwide Korean claims data for the period 2003 to 2013.The Korean National Health Insurance Service-National Sample Cohort is a stratified random sample of 1,025,340 subjects selected from among approximately 46 million Koreans. We evaluated the annual incidence of CPR per 1000 admissions in various age groups, hospital deaths preceded by CPR, and survival rate following in-hospital CPR. Analyses of the relationships between survival and patient and hospital characteristics were performed using logistic regression analysis.A total of 5918 in-hospital CPR cases from 2003 to 2013 were identified among eligible patients. The cumulative incidence of in-hospital CPR was 3.71 events per 1000 admissions (95% confidence interval 3.62-3.80). The CPR rate per 1000 admissions was highest among the oldest age group, and the rate decreased throughout the study period in all groups except the youngest age group. Hospital deaths were preceded by in-hospital CPR in 18.1% of cases, and the rate decreased in the oldest age group. The survival-to-discharge rate in all study subjects was 11.7% during study period, while the 6-month and 1-year survival rates were 8.0% and 7.2%, respectively. Survival tended to increase throughout the study period; however, this was not the case in the oldest age group. Age and malignancy were associated with lower survival rates, whereas myocardial infarction and diabetes mellitus were associated with higher survival rates.Our result shows that hospital deaths were preceded by in-hospital CPR in 18.1% of case, and the survival-to-discharge rate in all study subjects was 11.7% during the study period. Survival tended to increase throughout the study period except for the oldest age group. Our results provide reliable data that can be used to inform judicious decisions on the implementation of CPR, with the ultimate goal of optimizing survival rates and resource utilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".