Ventricular Assist Device Training and Emergency Management Among Pediatric Cardiac Intensive Care Physicians – Multicenter Cross-Sectional Survey
Bibliographic record
Abstract
Background/Aim: Pediatric cardiac intensive care physicians practicing at centers that implant ventricular assist devices (VAD's) are exposed to increasing numbers of VAD patients, with a significant number of VAD-days. We aimed to delineate pediatric cardiac critical care practices surrounding routine and emergency management of VADs. Methodology: We administered a multicenter cross-sectional survey of pediatric cardiac intensive care unit (CICU) physicians in the United States and Canada. Survey distribution occurred between August 31st and October 26th 2021. Results: A total of 254 CICU physicians received a formal invitation to participate, with 108 returning completed surveys (42.5% response rate). Responses came from CICU attending physicians at 26 separate institutions. Respondents' level of experience was well distributed across junior, mid-level, and senior staff: less than 5 years (38%), 5-9 years (25%), and >/= 10 years (37%). Most respondents had received formal training in the management of VAD patients (n = 93, 86.1%), with training format including fellowship (61%), simulation (36%), and national/international conferences (26.5%). Dedicated advanced cardiac therapies teams were available at the institutions of 97.2% of respondents. A total of 78/108 (72.2%) described themselves as “comfortable” or “very comfortable” in pediatric VAD management. While 63% (68/108) of respondents reported that they had never performed (or overseen the performance of) chest compressions in a pediatric patient with a VAD, 37% (40/108) reported performing CPR at least once in a VAD patient. Conclusion: With no existing international guidelines for emergency cardiovascular care in the pediatric VAD population, our survey identifies an important gap in resuscitation recommendations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".