Evaluating Mortality Risk Adjustment Among Children Receiving Extracorporeal Support for Respiratory Failure
Bibliographic record
Abstract
This study evaluates whether three commonly used pediatric intensive care unit (PICU) severity of illness scores, pediatric risk of mortality score (PRISM) III, pediatric index of mortality (PIM) 2, and pediatric logistic organ dysfunction (PELOD), are the appropriate tools to discriminate mortality risk in children receiving extracorporeal membrane oxygenation (ECMO) support for respiratory failure. This study also evaluates the ability of the Pediatric Risk Estimate Score for Children Using Extracorporeal Respiratory Support (Ped-RESCUERS) to discriminate mortality risk in the same population, and whether Ped-RESCUERS' discrimination of mortality is improved by additional clinical and laboratory measures of renal, hepatic, neurologic, and hematologic dysfunction. A multi-institutional retrospective cohort study was conducted on children aged 29 days to 17 years with respiratory failure requiring respiratory ECMO support. Discrimination of mortality was evaluated with the area under the receiver operating curve (AUC); model calibration was measured by the Hosmer-Lemeshow goodness of fit test and Brier score. Admission PRISM-III, PIM-2, and PELOD were found to have poor ability to discriminate mortality with an AUC of 0.56 [0.46-0.66], 0.53 [0.43-0.62], and 0.57 [0.47-0.67], respectively. Alternatively, Ped-RESCUERS performed better with an AUC of 0.68 [0.59-0.77]. Higher alanine aminotransferase, ratio of the arterial partial pressure of oxygen the fraction of inspired oxygen, and lactic acidosis were independently associated with mortality and, when added to Ped-RESCUERS, resulted in an AUC of 0.75 [0.66-0.82]. Admission PRISM-III, PIM-2, and PELOD should not be used for pre-ECMO risk adjustment because they do not discriminate death. Extracorporeal membrane oxygenation population-derived scores should be used to risk adjust ECMO populations as opposed to general PICU population-derived scores.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".