Volume versus outcome: More emergency medical services personnel on-scene and increased survival after out-of-hospital cardiac arrest
Bibliographic record
Abstract
© 2015 Elsevier Ireland Ltd. Background and aim: The large regional variation in survival after treatment of out-of-hospital cardiac arrest (OHCA) is incompletely explained. Communities respond to OHCA with differing number of emergency medical services (EMS) personnel who respond to the scene. The effect of different numbers of EMS personnel on-scene upon outcomes is unclear. We sought to evaluate the association between number of EMS personnel on-scene and survival after OHCA. Methods: We performed a retrospective review of prospectively collected data on 16,122 EMS-treated OHCA events from December 1, 2005 to May 31, 2007 from a combined population over 21 million people residing in an area of over 33,000 square miles in Canada and the United States. Number of EMS personnel on-scene was defined as the number of EMS personnel who responded to the scene of OHCA within 15. min after 9-1-1 call receipt and prior to patient death or transport away from the scene. Associations with survival to hospital discharge were assessed by using generalized estimating equations to construct multivariable logistic regression models. Results: Compared to a reference number of EMS personnel on-scene of 5 or 6, 7 or 8 EMS personnel on-scene was associated with a higher rate of survival to hospital discharge, adjusted odds ratio [OR], 1.35 (95% CI: 1.05, 1.73). There was no significant difference in survival between 5 or 6 personnel on-scene versus fewer. Conclusion: More EMS personnel on-scene within 15. min of 9-1-1 call was associated with improved survival of out-of-hospital cardiac arrest. It is unlikely that this finding was mediated solely by earlier CPR or earlier defibrillation.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".