A Meta-analysis of the Effect of Unprofessional Early Defibrillation on the Survival Rate of Patients with Out-of-hospital Cardiac Arrest
Bibliographic record
Abstract
Background At this stage, cardiovascular disease is still the main cause of death among residents in China, about 10 000 people die from out-of-hospital cardiac arrest every year, and the survival rate of patients with out-of-hospital cardiac arrest is less than 5%. Early defibrillation has provided a new method and approach for improving the prognosis of patients with out-of-hospital cardiac arrest and reducing the mortality rate. However, the existing research conclusions are still controversial, and there is no relevant systematic review in China. Objective Meta-analysis was used to explore the effect of early defibrillation of on-site first responders on the survival rate of out-of-hospital cardiac arrest patients. Methods PubMed, The Cochrane Library, EMBase, CINAHL, CNKI, Wanfang Data and VIP database were retrieved by computer from database establishment to October 2021. A case-control study or cohort study of the impact of AED use on the patient's weekly survival and discharge rate in field first responders was collected, quality evaluation of included case-control studies using the Cochrane Systematic Evaluation Method and quality evaluation of included cohort studies using the New Castle-Ottawa Scale (NOS) . Meta-analysis was performed using Revman 5.4 software. Results 12 articles were finally included, covering 29 605 patients. The overall quality of the articles was above the medium level. The results of the meta-analysis showed that, the weekly survival and discharge rate of out-of-hospital cardiac arrest patients with early defibrillation by first responders on site was higher than that of out-of-hospital cardiac arrest patients without early defibrillation by first responders on site〔OR=0.47, 95%CI (0.45, 0.50) , P<0.001〕, the weekly survival and discharge rate of out-of-hospital cardiac arrest patients with early defibrillation by professional first responders was higher than that of out-of-hospital cardiac arrest patients with early defibrillation by non-professional first responders〔OR=0.72, 95%CI (0.65, 0.81) , P<0.000 01〕. Conclusion Early defibrillation of first responders on site can effectively improve the weekly survival and discharge rate of out-of-hospital cardiac arrest patients, professional first responders who have received early defibrillation training can improve the weekly survival and discharge rate of out-of-hospital cardiac arrest patients compared with non-professional first responders. Due to limitations in the quality and quantity of included studies, the above conclusion needs to be verified by conducting more prospective cohort studies with large samples.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.065 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".