Improving Communication Between Surgery and Critical Care Teams: Beyond the Handover
Bibliographic record
Abstract
BACKGROUND: Structured communication tools for postoperative surgical handover to the intensive care unit (ICU) have shown promise, yet little work has addressed ongoing daily communication between the surgery and ICU teams thereafter. OBJECTIVES: Evaluation of a novel, 2-part communication intervention between surgery and ICU teams focused on postoperative handover and ongoing daily communication. METHODS: A mixed-methods, pre- and postintervention survey study was conducted in a closed quaternary medical-surgical ICU. Study participants (N = 112) included ICU physicians, nurses, allied health professionals, and physicians on the surgical team. The intervention consisted of a handover checklist completed postoperatively on arrival in the ICU and a 5-item communication tool completed daily by the surgical team. RESULTS: = .008). No significant improvement was seen in communication regarding disposition or overall improvement in patient safety risk from communication errors. CONCLUSIONS: A simple handover checklist improved health care practitioner satisfaction with communication during postoperative handover to the ICU. Concise daily communication tools are an appropriate option for improving ongoing communication between surgeons and the ICU team thereafter.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".