Comparison of manual chest compression versus mechanical chest compression for out-of-hospital cardiac arrest: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Out-of-hospital cardiac arrest is a life-threatening condition that requires immediate intervention to increase the prospect of survival. There are various ways to achieve cardiopulmonary resuscitation in such patients, either through manual chest compression or mechanical chest compression. Thus, we performed a systematic review and meta-analysis to investigate the differences between these interventions. METHODS: PubMed, Cochrane Library, and Scopus were explored from inception to May 2023. Additionally, the bibliographies of relevant studies were searched. The Cochrane Risk of Bias Tool for Randomized Controlled Trials, Newcastle-Ottawa Scale, and the Risk of Bias in Non-Randomized Studies-I tools were utilized to perform quality and risk of bias assessments. RESULTS: There were 24 studies included within this quantitative synthesis, featuring a total of 111,681 cardiac arrest patients. Overall, no statistically significant differences were observed between the return of spontaneous circulation, survival to hospital discharge, short-term survival, and long-term survival. However, manual chest compression was associated with a significantly superior favorability of neurological outcomes (OR: 1.41; 95% CI: 1.07, 1.84; P = .01). CONCLUSION: Although there were no major differences between the strategies, the poorer post-resuscitation neurological outcomes observed in mechanical chest compression indicate the need for further innovation and advancements within the current array of mechanical devices. However, future high-quality studies are necessary in order to arrive at a valid conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.045 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".