C.5 Altered inflammatory profiles in critically ill children with neurologic involvement
Bibliographic record
Abstract
Background: More than 1 in 4 children admitted to the pediatric ICU (PICU) have suspected neuroinflammation for a variety of reasons. While often beneficial, uncontrolled inflammation can lead to secondary neurologic injuries and interfere with repair mechanisms. Methods: A prospective cohort study was initiated at Alberta Children’s Hospital to evaluate neuroinflammation in children admitted to the PICU. Forty-eight cytokines, chemokines and growth factors collected at multiple pre-determined timepoints were analyzed along with data on clinical trajectory. Preliminary exploratory analyses of patients enrolled January 2022-July 2023 were completed. Results: Fifty-three patients were included in the initial analysis. Encephalopathy (18.9%), hypoxia (17%) and TBI (15.1%) were the most common reasons for enrollment. All groups had temporal alterations in serum cytokines, with primary inflammatory brain diseases having the highest levels of innate inflammation (cytokine storm) on admission and day one compared to other subgroups. There was a trend towards normalization of cytokine levels over time. Conclusions: Temporal profiling of cytokine levels can inform on neuroinflammatory pathways contributing to the clinical course in critically ill children. Further analysis is ongoing with the entire cohort to evaluate longitudinal and between-group differences. Improved understanding of altered neuroinflammatory pathways in this population may assist with rationalizing targeted immunotherapies to improve outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".