Clinical Outcomes of Pharmacist Involvement in Cardiac Arrest and Trauma Resuscitations: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The role of clinical pharmacists in the emergency department continues to gain recognition, particularly during cardiac and trauma resuscitations. However, their contributions to patient outcomes remain unclear. The objective of this scoping review with narrative synthesis was to determine the impact of pharmacists on medication and patient outcomes during cardiac and trauma resuscitations and to identify barriers to integration. METHODS: A literature search of databases in September 2024 identified randomized and non-randomized control trials, evaluating the impact of pharmacists' involvement in cardiac or trauma resuscitations. Excluded were studies on acute stroke, acute hemorrhage, and sepsis. Data were extracted and analyzed for primary (e.g., medication errors and Advanced Cardiovascular Life Support [ACLS] compliance) and secondary outcomes (e.g., pharmacists' education and training). RESULTS: Of the 560 records screened, 26 records were included in the final analysis. Due to heterogeneity, quantitative analysis was not feasible. Among primary outcomes, ACLS guideline compliance and medication errors were commonly reported; mortality and length of stay were less commonly reported. ACLS certification improved pharmacists' confidence in their tasks. Pharmacists' presence also correlated with reduced healthcare costs. CONCLUSIONS: Our analysis suggests that the involvement of pharmacists in the context of emergency cardiac or trauma resuscitations may benefit direct patient outcomes and indirect outcomes.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".