Abstract 71: Diffusion Imaging Of Cerebral Diaschisis In Neonatal Arterial Ischemic Stroke
Bibliographic record
Abstract
Objectives: Diffusion MRI (DWI) corticospinal changes remote from neonatal arterial ischemic stroke (NAIS) correlate with physiology and outcome. We hypothesized that DWI can quantify acute alterations in other remote connected structures (“diaschisis”). Methods: Children from Calgary and SickKids Stroke Programs were studied with unilateral NAIS, DWI <10 days from term birth and follow up (Pediatric Stroke Outcome Measure). Image J thresholding quantified DWI diaschisis in connected structures according to validated methods (figure). Subsequent atrophy was measured volumetrically on MRI >12 mos (OsiriX). Primary outcome was total diaschisis signal (TD) which was then corrected for stroke volume (% brain infarcted). Scores were regionalized by cerebral structure. Associations with MRI timing and outcome were sought (nonparametric statistics). Method reliability was confirmed. Results: Twenty neonates met criteria (55% male). Median age at MRI was 72 hours. Diaschisis was common, observed in 16 (80%). Thalamic diaschisis was most common (100%), followed by callosal (50%) and striatal (15%). Perilesional diaschisis estimates were highly variable. Structures manifesting acute diaschisis atrophied on follow-up imaging. TD correlated with stroke volume (p=0.001). TD correlation to poor outcome (p=0.01) did not persist with correction for infarct volume. TD did not correlate with age at MRI. Method reliability was good (ρ >0.80). Conclusion: DWI diaschisis is common and measureable in NAIS. As a possible early imaging marker of network injury, larger studies are required to determine clinical relevance.
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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 | 0.000 |
| 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) | 0.002 | 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".