EURECA One 2014: ROSC analysis
Bibliographic record
Abstract
INTRODUCTION Cardiac arrest survival rate after out of hospital cardiopulmonary resuscitations is pretty low and goes from 1,7% to 6,1%. The return of spontaneous circulation is the first step towards complete recovery of the patient after experiencing cardiac arrest. Predictors of sustainable ROSC include witnessing of collapse by laymen, initial heart rhythm, bystander initiation of CPR, early EMS engagement, early defibrillation and short duration of medical transport. AIM: It is to investigate the return of ROSC in regards to epidemiological and demographic characteristics, treatment and outcome (survival after a month) with shockable initial rhythm for a six-month sample. METHOD: Prospective observational study where data were gathered through particular questionnaire that concerned OHCA (out-of-hospital cardiac arrest) on the territory of Vojvodina during six month period (from October 1st 2104. until March 31st 2015.). The data that were used are registered in the data base of European programme EuReCA One 2014. RESULTS: 276 patients that have had OHCA on the territory of Vojvodina were analyzed - the incidence of 40,63 per 100 000. EMS conducted CPR in 51,16% (N=155, n=22,82/100 000), ROSC was established in 30,32% (N=47, n=6,92/100 000), and 30 days survival was documented in 9,03% of the cases (N=14, n=2,06/100 000). Shockable rhythm was initially recognized with 59,57% of the patients (N=28, n=4,10/100 000). CONCLUSION: Cardiopulmonary resuscitation sets the return of spontaneous circulation (ROSC) as its primary objective, which depends great deal on key factors affecting the course of CPR, and positive variables for CPR course are initial shockable rhythm, witnessing of cardiac arrest by layman or EMS, heart condition as the presumed cause, female gender and age under 80 years.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".