DIFFUSION RESTRICTION IN REMOTE DESCENDING CORTICOSPINAL TRACTS PREDICTS MOTOR OUTCOME IN NEONATAL ARTERIAL ISCHEMIC STROKE
Bibliographic record
Abstract
Objectives: The neonatal period is the most common time for arterial ischemic stroke (AIS) in children. Most children are left with motor deficits but accurate predictors of outcome are lacking. Diffusion-weighted MR imaging (DWI) may detect descending corticospinal tract (DCST) changes distal from AIS location that may represent pre-Wallerian degeneration (WD) (DeVries, 2005). Our aim was to quantify DWI changes in the DCST and correlate them with motor outcome. Methods: Fourteen consecutive neonates with AIS, acute DWI, and no other neurological insult were included. A system of quantitative measures of DWI signal change through the DCST (internal capsule to medullary pyramids) was developed using Image J software. Motor outcomes were scored using the PSOM at a minimum follow-up of 12 months and correlations to the DCST measures were sought using Fisher's exact test. Results: DWI signal in the DCST was quantifiably different from the contralateral side in 9 of 14 neonates. Locations included the internal capsule and cerebral peduncle as well as pontine and medullary DCST. Motor outcome was highly correlated with involvement of the middle third of the cerebral peduncle (p=0.003), the percentage of peduncle affected (p=0.005), and the length of DCST affected (p=0.04). All patients with poor outcome had >25% of the peduncle and >20mm of DCST affected, while neither of these were seen in any child with good outcome. All children without DCST DWI signal had normal outcome while all of those with poor outcome demonstrated WD of the peduncle on follow-up. DWI signal differences were often not evident on visual inspection alone. Conclusion: Computer-assisted quantification of DWI signal in the DCST remote from the area of infarction appears to predict motor outcome in neonatal AIS.
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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 | 0.006 |
| 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".