Abstract 14028: Stroke Location, White Matter Disconnections, and Neurodevelopmental Outcomes in Infants With Congenital Heart Disease
Bibliographic record
Abstract
Objectives: Infants with congenital heart disease (CHD) are at risk for stroke, although associations with neurodevelopmental outcomes have varied. In adults with stroke, location and altered structural connectivity (disconnections) have been linked to neurologic symptoms. We assessed whether stroke (1) location and (2) white matter disconnections are related to neurodevelopment in infants with CHD. Methods: Infants underwent pre- and post-operative brain MRI in prospective studies at 3 centres (UBC, UCSF, SickKids) and completed 18-month neurodevelopmental assessments using Bayley Scales with separate motor and cognitive composites. Adverse outcomes were defined as composite score <85. Strokes were manually segmented. Voxel-wise lesion symptoms maps (VLSM) were used to assess relationships between lesion location and neurodevelopment. Disconnectomes were generated using stroke segmentations as seeds for tractography and diffusion MRI from healthy controls. Voxel-wise disconnectome-symptoms maps (VDSM) of associations between structural disconnections and neurodevelopment were developed. Results: 50 infants with strokes had neurodevelopmental outcomes data. Most strokes were small (<1/3 of arterial territory; n= 33) and in the left MCA territory (Fig 1A). However, strokes in the right basal ganglia and adjacent central white matter were most strongly associated with adverse motor and cognitive outcomes (Fig 1B). White matter disconnections extended beyond areas of injury (Fig 1C); these were extensive even in small strokes. Disconnections of tracts passing through the right basal ganglia were most strongly associated with adverse motor and cognitive outcomes (Fig 1D). Conclusions: Strokes involving the basal ganglia, particularly in the right hemisphere, were associated with worse motor and cognitive outcomes in infants with CHD. These data draw increasing attention to the right hemisphere in early-life stroke and neurodevelopment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".