Does delivering chest compressions to patients who are not in cardiac arrest cause unintentional injury? A systematic review
Bibliographic record
Abstract
Background: Chest compressions are life-saving in cardiac arrest but concern by layperson of causing unintentional injury to patients who are not in cardiac arrest may limit provision and therefore delay initiation when required. Aim: To perform a systematic review of the evidence to identify if; among patients not in cardiac arrest outside of a hospital, does provision of chest compressions from a layperson, compared to no use of chest compressions, worsen outcomes. Method: We searched Medline (Ovid), Web of Science Core Collection (clarivate) and Cinahl (Ebsco). Outcomes included survival with favourable neurological/functional outcome at discharge or 30 days; unintentional injury (e.g. rib fracture, bleeding); risk of injury (e.g. aspiration). ROBINS-I was used to assess for risk of bias. Grading of Recommendations, Assessment, Development and Evaluation methodology was used to determine the certainty of evidence. (PROSPERO registration number: CRD42023476764). Results: From 7832 screened references, five observational studies were included, totaling 1031 patients. No deaths directly attributable to chest compressions were reported, but 61 (6 %) died before discharge due to underlying conditions. In total, 9 (<1%) experienced injuries, including rib fractures and different internal bleedings, and 24 (2 %) reported symptoms such as chest pain. Evidence was of very low certainty due to risk of bias and imprecision. Conclusion: Patients initially receiving chest compressions by a layperson and who later were determined by health care professionals to not be in cardiac arrest rarely had injuries from chest compressions.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".