Exploring nurse-led cardiopulmonary resuscitation in the emergency department: A scoping review
Bibliographic record
Abstract
AIM OF THE REVIEW: The aim is to map the existing evidence on nurse-led resuscitation and identify gaps to inform future research directions. This scoping review critically examines the role of nurse-led resuscitation in the emergency department (ED). The review identifies the integral role of nurses in resuscitation teams, evaluates nurses' performance during resuscitation, and highlights the need for further research. METHOD: The review employed a comprehensive search strategy across multiple databases, including MEDLINE, CINAHL, PROSPERO, and EMBASE, along with sources of unpublished studies and grey literature such as ProQuest Theses, Grey Matters, Policy Commons, and Google Scholar. The search covered literature from 1993 to 2023. RESULTS: The searches returned a total of 494 citations. A total of 8 full articles met the inclusion criteria for data extraction and synthesis. Three key themes emerged from the review: (1) the integral role of nurses in resuscitation teams, (2) nurses' performance during resuscitation and (3) the need for future research. CONCLUSION: This scoping review underscores the potential of nurse-led resuscitation in emergency care settings. Nurses' roles in resuscitation teams are integral, with performance comparable to that of physicians in multiple domains. However, the current evidence base is limited to literature reviews and simulated environments and highlights the necessity for further robust research. Future studies should explore interdisciplinary team dynamics, communication patterns, and the direct impact of nurse-led resuscitation on patient outcomes in real-world clinical settings.
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 | 0.022 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".