Prognostic Accuracy of Fetal MRI in Predicting Postnatal Neurodevelopmental Outcome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The superior diagnostic accuracy of fetal MR imaging in detecting fetal brain abnormalities has been previously demonstrated; however, the ability of fetal MR imaging to prognosticate postnatal outcome is not well-studied. We performed a retrospective analysis to determine the prognostic accuracy of fetal MR imaging in predicting postnatal neurodevelopmental outcome. MATERIALS AND METHODS: We identified all fetal MR imaging performed at the Children's Hospital of Eastern Ontario during a 10-year period and assessed agreement between prenatal prognosis and postnatal outcome. Prenatal prognosis was determined by a pediatric neurologist who reviewed the fetal MR imaging report and categorized each pregnancy as having a favorable, indeterminate, or poor prognosis. Assessment of postnatal neurodevelopmental outcome was made solely on the basis of the child's Gross Motor Function Classification System score and whether the child developed epilepsy. Postnatal outcome was categorized as favorable, intermediate, or poor. We also assessed the diagnostic accuracy of fetal MR imaging by comparing prenatal and postnatal imaging diagnoses. RESULTS: We reviewed 145 fetal MR images: 114 were included in the assessment of diagnostic accuracy, and 104 were included in the assessment of prognostic accuracy. There was 93.0% agreement between prenatal and postnatal imaging diagnoses. Prognosis was favorable in 44.2%, indeterminate in 50.0%, and poor in 5.8% of pregnancies. There was 93.5% agreement between a favorable prenatal prognosis and a favorable postnatal outcome. CONCLUSIONS: A favorable prenatal prognosis is highly predictive of a favorable postnatal outcome. Further studies are required to better understand the role of fetal MR imaging in prognosticating postnatal development, particularly in pregnancies with indeterminate and poor prognoses.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".