Abstract 75: Gliosis After Perinatal Stroke: Quantification And Outcomes
Bibliographic record
Abstract
Background: Perinatal stroke diseases cause most hemiparetic cerebral palsy: Neonatal Arterial Ischemic Stroke (NAIS), arterial presumed perinatal stroke (APPIS), and periventricular venous infarctions (PVI). Gliosis, a brain reaction to injury, may be both a marker of lesion timing and a potential barrier to cell-based therapies. We hypothesized that gliosis is measureable, comparable between NAIS and APPIS, and associated with poor outcome. Methods: Children from the Alberta Perinatal Stroke Project were included with: (1) unilateral NAIS, APPIS, or PVI, (2) axial FLAIR MRI >24mos of age, and (3) >24mos follow-up (Pediatric Stroke Outcome Measure). Novel ImageJ software protocols quantifying gliosis were developed (Figure). Gliosis scores (GS) corrected for infarct and brain volume were compared across stroke types, and outcomes (motor, overall). Results: Of 149 APSP children, 39 were studied (median 10 years, 51% male). GS ranged from 19-1191 (mean 369±361) and did not correlate with age at imaging. GS were comparable between NAIS (438±171) and APPIS (444±380, p=0.97). Arterial GS tended to be higher than PVI lesions (443±337 vs 266±378, p=0.15). Arterial GS correlated with good motor outcome (705±338 vs 312±259, p=0.008) but not overall outcome (p=0.53). PVI GS were not associated with overall or motor outcome. Method reliability was excellent (ρ=0.99). Conclusion: MRI quantification of gliosis is feasible in children with perinatal stroke. Comparable gliosis in NAIS and APPIS provide further indirect evidence of similar perinatal timing. The association of gliosis with good motor outcome warrants further study.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".