Abstract 188: Neuroprognostication After Pediatric Cardiac Arrest
Bibliographic record
Abstract
Objective: Management decisions and parental counseling following pediatric cardiac arrest depend on the ability of physicians to make accurate and timely predictions regarding neurological recovery. This study examined the level of agreement in neuroprognostication among neurologists and among intensivists, and prediction accuracy in terms of time post cardiac arrest. Methods: Pediatric neurologists (N=10) and intensivists (N=9) each reviewed 18 cases of children successfully resuscitated from a cardiac arrest and managed in the pediatric ICU. Cases were sequentially presented in three sections: Day 1, Days 2-4, and Days 5-7 post arrest, with updated examinations, neurophysiologic data, and neuroimaging data as available. At each time point, physicians predicted outcome by Pediatric Cerebral Performance Category (PCPC). Predicted PCPC (p) vs. actual hospital discharge PCPC (a) outcomes were compared. Prediction accuracy was defined as Exact Accuracy (p-a = 0) and Close Accuracy (p-a = ±1). Descriptive statistics, Kappa coefficients (Κ) and generalized estimating equation were performed. Results: Agreement among neurologists improved over time (Day 1 Κ 0.27, Days 2-4 Κ 0.43, Days 5-7 Κ 0.62), as did agreement among intensivists (Day 1 Κ 0.30, Days 2-4 Κ 0.44, Days 5-7 Κ 0.57). For all physicians, prediction accuracy did not improve from Day 1 to Days 2-4, but did improve from Days 2-4 to Days 5-7 (p=0.001) [Exact Accuracy Days 2-4 and Days 5-7: 35% vs. 43%; Close Accuracy Days 2-4 and Days 5-7: 77% vs. 89%]. Prediction accuracy did not differ significantly between physician groups at any time point (p=NS). At Days 5-7, 16 of 19 physicians predicted PCPC = 5-6 at least once when actual PCPC ≤ 4. All physicians predicted PCPC ≤ 4 at least once when actual PCPC = 5-6. Conclusions: Inter-rater agreement among neurologists and among intensivists improved over time and converged to moderate-good levels at later time points. For all physicians, prediction accuracy improved over time; however, even at later time points, incorrect outcome predictions occurred. The ethical implications of these findings for clinical decision-making and allocation of health care resources after pediatric cardiac arrest are considerable.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".