Identifying What Is Known About Improving Operating Room to Intensive Care Handovers: A Scoping Review
Bibliographic record
Abstract
The purpose is to provide a descriptive overview of relevant material exploring improvement of handovers from the operating room (OR) to intensive care unit (ICU). An online search (MEDLINE, Cochrane, EMBASE, CINAHL, and Joanna Briggs), including gray literature and relevant reference lists, was completed. In all, 4574 unique citations were screened and 155 full-text reviews performed; 65 articles were included in the final analysis. The majority of articles discuss an ideal structure for handover (n = 63; 97%); 43 (66%) articles mentioned strategies for implementing such an approach. Only 21 (32%) explicitly described formal quality improvement (QI) methods. Few explored project sustainability and impact of a structured handover on patient safety outcomes (n = 15, 23%). Published literature suggests that there is a significant gap in evidence of measured patient outcomes from standardization of OR to ICU handover processes. Identifying formal QI strategies used to sustain standardized handover processes will allow accurate measurement of patient outcomes.
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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".