Magnetic Resonance Imaging (MRI) and Prognostication in Neonatal Hypoxic-Ischemic Injury
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) could improve prognostication in neonatal brain injury; however, factors beyond technical or scientific refinement may impact its use and interpretation. We surveyed Canadian neonatologists and pediatric neurologists using general and vignette-based questions about the use of MRI for prognostication in neonates with hypoxic-ischemic injury. There was inter- and intra-vignette variability in prognosis and in ratings about the usefulness of MRI. Severity of predicted outcome correlated with certainty about the outcome. A majority of physicians endorsed using MRI results in discussing prognosis with families, and most suggested that MRI results contribute to end-of-life decisions. Participating neonatologists, when compared to participating pediatric neurologists, had significantly less confidence in the interpretation of MRI by colleagues in neurology and radiology. Further investigation is needed to understand the complexity of MRI and of its application. Potential gaps relative to our understanding of the ethical importance of these findings should be addressed.
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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 | 0.011 | 0.092 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".