P.039 Informing treatment advancement and innovation in a tertiary care neurocritical care (NCC)) program
Bibliographic record
Abstract
Background: Children with neurological injuries/insults carry the highest risk of death and disability in Pediatric and Neonatal ICUs. These patients comprise 25-30% of admissions and have a myriad of diagnoses. Longitudinal outcome data is required to inform treatment effects and innovation strategies. NCC at the Alberta Children’s Hospital (ACH) participates in acute, subacute, and long-term/outpatient management with an aim to use comprehensive clinical data to improve outcomes. Methods: A prospective, longitudinal, population-based observational cohort study of NCC patients from local NICUs, PICU, and a NCC follow-up program, with comprehensive data from clinical records, development scores and QoL assessments. Results: Since 2019, 929 patients have been enrolled including: 407 neonates, 167 infants, 106 preschool-age and 100 school-age children, and 152 adolescents. The most common reasons for NCC consult were paroxysmal events (36%), encephalopathy (27%) and neonatal HIE (20%). Conclusions: Our database encapsulates the diverse nature of NCC patients and has enabled cohort-specific studies (e.g., neonatal HIE and ECLS outcomes). Program evolution will further facilitate higher powered research studies through large enrollment, comprehensive data capture (with a provincial EHR), and longitudinal outcomes. Engagement with staff and families will also inform treatment and afford evidence-based counseling to families.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".