360: Glow with the Flow: Enhancing Visual Fidelity in ECMO Simulation Using Photochromic Dye and UV Modulation
Bibliographic record
Abstract
Purpose: Knowing there is an increasing demand for extracorporeal life support (ECLS), the goal of this study was to determine the ECLS training experience of U.S. pediatric ECLS fellowships (EF) and how this may influence future career paths. Methods: A 32-question electronic survey was distributed to current and previous fellows of known U.S. pediatric EF programs. Participants were anonymously queried on pertinent demographic data, motivation for pursuing an EF, experience during EF and current career position. Summary statistics were performed. Results: Of the 54 individual surveys distributed, 29 (54%) complete responses were obtained from 4 institutions. Twenty-five (86%) respondents identified as surgeons and 4 (14%) as critical care intensivists. EF completion date ranged from 1985-2023. During EF, 10 (37%) of fellows managed 10-20 ECLS patients during any one year and 5 (19%) managed > 50 patients. Clinical responsibilities during EF included determining ECLS candidacy (96%), leading rounds in the NICU and PICU (74% and 67%) and taking ECLS call (100%). Most (73%) EF programs did not have a formal ECLS-specific didactic curriculum. Twenty-one (96%) of graduates indicated comfort managing both a straightforward ECLS run or a complex run. Eighteen (75%) respondents agreed that an ECLS fellowship was critical to achieving career goals and 7 (37%) currently hold an ECLS leadership position at their institution. Conclusion: Pediatric EF greatly vary in regard to clinical, didactic and administrative experiences. As ECLS utilization continues to expand standardized pediatric specific EF training and benchmarks for certification should be considered.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".