Handover after pediatric heart surgery: A simple tool improves information exchange*
Bibliographic record
Abstract
OBJECTIVE: To improve the quality of handover of complex patients after pediatric cardiac surgery through the use of a simple handover tool. DESIGN: Prospective, pre-/postinterventional. SETTING: A tertiary care, pediatric intensive care unit in North America. SUBJECTS: Thirty-three consenting healthcare providers from pediatric cardiac anesthesia, critical care, and cardiothoracic surgery participating in 31 handovers. INTERVENTION: A fill-in-the-blank, one-page tool was developed to guide the information transmitted by the surgeon and anesthesiologist to the pediatric intensive care unit team during handover of postcardiac surgery patients. MEASUREMENTS AND MAIN RESULTS: Total handover score, four subscores, handover duration, and postoperative high-risk events were measured before and after introducing the tool into clinical practice. The patients in both the pre- and postintervention groups were similar at baseline. The total handover score (maximum 43 points) improved significantly after the implementation of the handover tool (28.2 of 43 ± 4.6 points vs. 33.5 of 43 ± 3.7 points, p = .002). There was also a significant improvement in the medical (8.3 ± 2.6 vs. 10.3 ± 2.1 points, p = .024) and surgical (7.5 ± 1.4 vs. 9.3 ± 1.6 points, p = .002) intraoperative information subscores. Use of the tool did not prolong handover duration (8.3 ± 4.6 vs. 11.1 ± 3.9 mins, p = .1). There was a trend toward more patients being free from high-risk events in the postintervention group (31.2% vs. 6.7%), but this did not reach statistical significance (p = .1). CONCLUSIONS: Use of a simple tool during handover of pediatric postcardiac surgery patients resulted in a more complete exchange of critical information with no significant prolongation of the handover duration.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".