Cold Debriefings after In-hospital Cardiac Arrest in an International Pediatric Resuscitation Quality Improvement Collaborative
Bibliographic record
Abstract
INTRODUCTION: Clinical event debriefing functions to identify optimal and suboptimal performance to improve future performance. "Cold" debriefing (CD), or debriefing performed more than 1 day after an event, was reported to improve patient survival in a single institution. We sought to describe the frequency and content of CD across multiple pediatric centers. METHODS: Mixed-methods, a retrospective review of prospectively collected in-hospital cardiac arrest (IHCA) data, and a supplemental survey of 18 international institutions in the Pediatric Resuscitation Quality (pediRES-Q) collaborative. Data from 283 IHCA events reported between February 2016 and April 2018 were analyzed. We used a Plus/Delta framework to collect debriefing content and performed a qualitative analysis utilizing a modified Team Emergency Assessment Measurement Framework. Univariate and regression models were applied, accounting for clustering by site. RESULTS: CD occurred in 33% (93/283) of IHCA events. Median time to debriefing was 26 days [IQR 11, 41] with a median duration of 60 minutes [20, 60]. Attendance was variable across sites (profession, number per debriefing): physicians 12 [IQR 4, 20], nurses 1 [1, 6], respiratory therapists 0 [0, 1], and administrators 1 [0, 1]. "Plus" comments reported per event were most commonly clinical standards 47% (44/93), cooperation 29% (27/93), and communication 17% (16/93). "Delta" comments were in similar categories: clinical standards 44% (41/93), cooperation 26% (24/93), and communication 14% (13/93). CONCLUSIONS: CDs were performed after 33% of cardiac arrests in this multicenter pediatric IHCA collaborative. The majority of plus and delta comments could be categorized as clinical standards, cooperation and communication.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".