Modifiable Survival Factors of Out-of-Hospital Cardiac Arrest among Global Population: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Out-of-hospital cardiac arrest (OHCA) is the most common type of cardiac arrest and causing much mortality and burden even preventive measure has been made. Therefore, we conducted study to reduce OHCA morbidity and mortality by finding modifiable survival factors in-order to interfere them. We did systematic review of large cohort studies (n>100,000) on general population from four databases, then filtered 3,560 studies into 9 studies and appraised them using Newcastle-Ottawa scale for quality and Cochrane risk-of-bias before being synthesized. Among 486,012 subjects, we found out that age and shockable rhythm is unmodifiable but could be helped with lifestyle. Modifiable factors are grouped into two: bystander response including public location (OR=1.24; CI 95%=1.16–1.32), bystander witness (OR=1.45; CI 95%=1.36–1.56), bystander CPR (OR=1.45; CI 95%=1.36–1.56); and emergency service delivery including paramedic response <10 minutes (OR=1.55; CI 95%=1.41–1.70), ambulance physician (OR=1.52; CI 95%=1.37–1.68). Having OHCA in public means bigger chance of being resuscitated. However, resuscitation by uneducated bystander shown harmful thus public education was needed. Emergency services were considered important to arrive with competent workers, especially physicians who was trained on defibrillator usage and management regiment. Therefore, increasing public awareness, provide more ambulance and district health center facility, and training of health care workers are essential. In conclusion, management of OHCA involved multidisciplinary action throughout the nation to increase outcome of OHCA and lessen the burden. More area-specified and factor-specified studies should be conducted to improve applicability.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".