Key Considerations in Establishing a Pediatric Rescue Extracorporeal Life Support Program: A Multi Methods Review
Bibliographic record
Abstract
Extracorporeal life support (ECLS) is generally limited to centers with cardiac surgery. However, pediatric centers without cardiac surgery can still provide potentially lifesaving ECLS through a Rescue Program, allowing a local team to cannulate and stabilize patients before they are transported to a center with cardiac surgery support for ongoing care. This multimethod study provides an exploration of pediatric ECLS team insights regarding program implementation and offers recommendations for other centers wishing to develop a similar program. We performed surveys and semi-structured interviews to gather perspectives from ECLS team members. Demographics and preliminary perspectives were obtained from surveys. Interviews were transcribed and coded using thematic analysis to identify key considerations, facilitators, and barriers related to rescue program implementation. Our multidisciplinary ECLS team perceived great value in the rescue program and identified elements critical to successful program development and implementation, including barriers that might exist for any center wishing to set up a similar program. Participants emphasized that the initial design and continued maintenance of any Rescue ECLS Program be a comprehensive, multidisciplinary initiative. Clear communication, a mechanism for debriefing and feedback, and a strategy allowing for flexible program evolution are essential.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| 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) | 0.003 | 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".