Abstract 2013: Predictors of Out-of-hospital Cardiac Arrest Survival: Influence of The Utstein Measures.
Bibliographic record
Abstract
Background The Utstein elements provide measures for comparing out-of-hospital cardiac arrest (OHCA) care and outcomes within and across communities. Whether these measures sufficiently predict survival and explain variation in outcome is not well studied. Hypotheses Utstein data elements predict survival to hospital discharge but explain only a portion of survival variation. Methods Design Prospective, population-based cohort using uniform data definitions. Setting Ten North American sites participating in the Resuscitation Outcomes Consortium, a prehospital emergency care network. Population Persons ≥ 20 years with OHCA from 12/1/2005 – 11/30/2006 (n=9582). Analyses We used logistic regression to assess association between Utstein elements and survival and to determine whether they accounted for site survival differences. We evaluated measures of residual variance to assess what proportion of outcome variation the Utstein elements predicted. Results Survival was 7.0% overall. Utstein elements were associated with survival (Table ). Relationships were similar when restricted to those with ventricular fibrillation. In analyses assessing variance of residuals, Utstein elements collectively accounted for 15% of variation in survival. Site was also associated with survival, ranging from 3.7% to 15.6%. In multivariable analyses, site association was not attenuated by adjusting for Utstein elements. Conclusion Although Utstein elements predict survival, they account for a modest portion of variation overall and do not explain survival differences across sites. Additional factors should be investigated to understand and potentially improve OHCA outcome.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".