Bibliographic record
Abstract
What Is the Issue? People who experience cardiac arrest need immediate care. Cardiopulmonary resuscitation (CPR), performed manually by a human rescuer, is a first line of treatment. Automated chest compression devices, also known as mechanical CPR devices, are designed to deliver high-quality, consistent compressions and may be of particular interest in settings with limited human rescuers or long travel times in emergency vehicles. However, whether these devices are more clinically or cost-effective than manual chest compressions is unclear. What Did We Do? To inform decisions about the use of automated chest compression devices for chest compressions (described as “automated chest compressions” in this report) compared to manual chest compressions, Canada’s Drug Agency (CDA-AMC) conducted a Rapid Review to identify and summarize the literature about the clinical and cost-effectiveness of automated chest compression devices in people of any age. We also aimed to identify evidence-based recommendations for the use of automated chest compression devices. We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2020. Two reviewers screened articles for inclusion based on predefined criteria, and 1 reviewer critically appraised the included studies and narratively summarized the findings. What Did We Find? We identified 4 systematic reviews (SRs) that examined the clinical effectiveness or safety of automated chest compressions via AutoPulse or Lund University Cardiopulmonary Assist System (LUCAS) devices compared to manual chest compressions, and 2 guidelines with recommendations for the use of automated chest compression devices overall. We did not find information for other devices licensed for sale in Canada. We did not find economic evaluations on the cost-effectiveness of automated chest compression devices. Clinical evidence showed mixed results on survival, neurologic outcomes, and return to spontaneous circulation between automated chest compressions and manual chest compressions, and a potential increase in harms with the use of these devices. However, these findings are based on heterogeneous evidence of variable quality and should be interpreted with caution. Evidence-based guidelines do not recommend the routine use of automated chest compression devices. They indicate that these devices could be applied under specific circumstances, such as when high-quality compressions are impractical or a danger to health care workers, provided professionals are trained and have experience with the device. Evidence was largely based on studies conducted outside of Canada, making the generalizability of the evidence unclear. One primary study found in 3 of the 4 SRs had a population from Canada, and no other clinical evidence was from Canada. No evidence-based guidelines were found from Canadian organizations. Most of the evidence did not include details about study participant demographics or dimensions of diversity or information specifically for rural, remote, territorial hospital, nurse-led hospital, small community, or tertiary care settings. The applicability of the evidence is unknown, including the potential benefits or harms in people with different sexes or genders; different ethnic, religious, educational, socioeconomic, or cultural backgrounds; or with limited access to health care services or in resource-limited settings. What Does This Mean? Health care professionals can consider following the recommendations from evidence-based guidelines, which do not encourage the routine use of automated chest compression devices, except under specific circumstances. This aligns with the heterogeneous clinical evidence identified from the included SR. Because there was no evidence found on cost-effectiveness or information on the clinical effectiveness for people with different sexes or genders; people from different ethnic, religious, educational, socioeconomic, or cultural backgrounds in Canada, or contexts such as rural, remote, or low-staff settings, decision-makers may wish to consider whether the potential benefits and harms from the evidence in this report are applicable to their local context before more high-quality evidence for Canadian settings is available.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".