Quantified Corticospinal Tract Diffusion Restriction Predicts Neonatal Stroke Outcome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neonatal arterial ischemic stroke occurs in > or =1:4000 births. Many children experience motor deficits but acute predictors of outcome are lacking. Diffusion-weighted MRI changes in descending corticospinal tracts remote from arterial ischemic stroke may represent pre-Wallerian degeneration. We verify and quantify this signal and correlate it with motor outcome. METHODS: Fourteen neonates with acute arterial ischemic stroke and > or =12 months follow-up with the Pediatric Stroke Outcome Measure were included. Quantitative measurements of descending corticospinal tracts diffusion-weighted MRI signal were developed using Image J software. RESULTS: Ipsilesional descending corticospinal tract diffusion-weighted MRI signal was abnormal in 10 neonates with decreased apparent diffusion coefficients (P<0.001). Poor outcome correlated with: (1) percentage of peduncle (P=0.002); (2) length of descending corticospinal tracts P<0.001); and (3) volume of descending corticospinal tracts (P=0.002). None of: (1) any peduncle; (2) any posterior limb of the internal capsule; or (3) infarct volume correlated with outcome. All children without descending corticospinal tracts signal had normal outcome. Chronic Wallerian degeneration was seen in all children with hemiparesis. Software-assisted analysis was superior to visual inspection with excellent reliability (intra-class correlation coefficient > or =0.9). CONCLUSIONS: Descending corticospinal tracts diffusion-weighted MRI signal is predictive of motor outcome from neonatal arterial ischemic stroke. This accurate, reliable, and simple tool will impact decision making in acute neonatal stroke.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".