Does delivering chest compressions to patients who are not in cardiac arrest cause unintentional injury? A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it