Diffusion imaging of cerebral diaschisis in neonatal arterial ischemic stroke
Bibliographic record
Abstract
Background: Neonatal arterial ischemic stroke (NAIS) is a leading cause of brain injury and cerebral palsy. Diffusion-weighted imaging (DWI) has revolutionized NAIS diagnosis and outcome prognostication. Diaschisis refers to changes in brain areas functionally connected but structurally remote from primary injury. We hypothesized that acute DWI can demonstrate cerebral diaschisis and evaluated associations with outcome. Methods: Subjects were identified from a prospective, population-based research cohort (Calgary Pediatric Stroke Program). Inclusion criteria were unilateral middle cerebral artery NAIS, DWI MRI within 10 days of birth, and >12-month follow-up (Pediatric Stroke Outcome Measure, PSOM). Diaschisis was quantified using a validated software method. Diaschisis-scores were corrected for infarct size and compared to outcomes (Mann-Whitney). Results: From 20 eligible NAIS, 2 were excluded for image quality. Of 18 remaining, 16 (89%) demonstrated diaschisis. Thalamus (88%) was most often involved. Age at imaging was not associated with diaschisis. Long-term outcomes available on 13 (81%) demonstrated no association between diaschisis score and PSOM categories. Conclusion: Cerebral diaschisis occurs in NAIS and can be quantified with DWI. Occurrence is common and should not be mistaken for additional infarction. Determining additional clinical significance will depend on larger samples with long-term outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".