Consensus Approach for Standardization of the Timing of Brain Magnetic Resonance Imaging and Classification of Brain Injury in Neonates With Neonatal Encephalopathy/Hypoxic-Ischemic Encephalopathy: A Canadian Perspective
Bibliographic record
Abstract
BACKGROUND: Neonatal encephalopathy (NE) and hypoxic-ischemic encephalopathy (HIE) are linked to significant neurodevelopmental impairments. Magnetic resonance imaging (MRI) is the preferred modality for classifying brain injury severity in HIE, yet considerable variability exists among institutions in terms of MRI timing, protocols, injury classification, and scoring systems for predicting long-term outcomes. METHODS: A Canadian taskforce comprising radiologists and neonatologists was established to develop a consensus on the optimal timing of brain MRI, appropriate MRI protocols, and a unified approach to the classification and scoring of brain injury in infants with NE secondary to hypoxic-ischemic insult. The taskforce proposed a radiological classification and scoring system that is both simplified and modified from previously validated systems. RESULTS: The consensus resulted in a standardized MRI protocol and a streamlined classification system designed to reduce interinstitutional variability. This proposed system offers a uniform framework for assessing the severity of brain injury and serves as a potential tool for predicting long-term neurodevelopmental outcomes. CONCLUSION: Once validated, the proposed radiological classification and scoring system can be applied across centers to facilitate consistent outcome comparisons, improve prognostication for neonates with NE/HIE, and enhance the quality of family counseling regarding long-term neurodevelopmental prospects.
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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 | 0.165 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".