Consensus Approach for Standardizing the Screening and Classification of Preterm Brain Injury Diagnosed With Cranial Ultrasound: A Canadian Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acquired brain injury remains common in very preterm infants and is associated with significant risks for short- and long-term morbidities. Cranial ultrasound has been widely adopted as the first-line neuroimaging modality to study the neonatal brain. It can reliably detect clinically significant abnormalities that include germinal matrix and intraventricular hemorrhage, periventricular hemorrhagic infarction, post-hemorrhagic ventricular dilatation, cerebellar hemorrhage, and white matter injury. The purpose of this article is to provide a consensus approach for detecting and classifying preterm brain injury to reduce variability in diagnosis and classification between neonatologists and radiologists. Our overarching goal with this work was to achieve homogeneity between different neonatal intensive care units across a large country (Canada) with regards to classification, timing of brain injury screening and frequency of follow up imaging. We propose an algorithmic approach that can help stratify different grades of germinal matrix-intraventricular hemorrhage, white matter injury, and ventricular dilatation in very preterm infants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it