Identification of Early Risk Factors for Mortality in Pediatric Veno-Arterial Extra Corporeal Membrane Oxygenation: The Patient Matters
Bibliographic record
Abstract
Objective: Pediatric Veno-Arterial Extra Corporeal Membrane Oxygenation (VA ECMO) is a life saving technology associated with high mortality. A successful VA ECMO course requires attention to multiple aspects of patient care, including ECMO and patient parameters. Early, potentially modifiable, risk factors associated with patient mortality should be analyzed and adjusted for when assessing VA ECMO risk profiles. Method: Retrospective single center experience of pediatric patients requiring VA ECMO from January 2021 to October 2023. Laboratory and ECMO flow parameters were extracted from the patients record and analyzed. Risk factors were analyzed using a Cox proportion hazard regression. Main Results: There were 45 patients studied. Overall survival was 51%. Upon uncorrected analysis there were no significant differences between the patients who survived and those who died. Utilizing a Cox proportion hazard regression, platelet count, fibrinogen level and creatine level were significant risk factors within the first twenty-four hours of a patient's ECMO course. Significance: Although we did not find a significant difference among ECMO flow parameters in this study, this work highlights that granular ECMO flow data can be incorporated to risk analysis profiles and potential modeling in pediatric VA ECMO. This study demonstrated, that when controlling for ECMO flow parameters, kidney dysfunction and clotting regulation remain key risk factors for pediatric VA ECMO mortality.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".