Population Based Birth Prevalence of Disease-Specific Perinatal Stroke
Bibliographic record
Abstract
BACKGROUND: Perinatal stroke encompasses multiple disease-specific cerebrovascular syndromes that cause lifelong neurodevelopmental morbidity for millions worldwide. Acute presentations include neonatal arterial ischemic stroke (NAIS), neonatal cerebral sinovenous thrombosis, and neonatal hemorrhagic stroke (NHS). Delayed presentations include arterial presumed perinatal ischemic stroke, periventricular venous infarction, and presumed perinatal hemorrhagic stroke. Our objective was to define the birth prevalence of all subtypes of perinatal stroke by using a population-based cohort. METHODS: The Alberta Perinatal Stroke Project is a research cohort established in 2008 in southern Alberta, Canada, with prospective (2008-2017) and retrospective (1990-2008) enrollment leveraging universal health care at a single tertiary care pediatric center. The primary outcome was the estimated birth prevalence of each perinatal stroke syndrome, secondary outcomes were birth prevalence over time, sex ratios, and change in age at diagnosis. Analysis included Poisson regression, Wilcoxon rank test, and Fisher exact test. RESULTS: The overall estimated birth prevalence of term-born perinatal stroke was 1:1100. The estimated birth prevalence was 1:3000 for NAIS, 1:7900 for arterial presumed perinatal ischemic stroke, 1:6000 for periventricular venous infarction, 1:9100 for cerebral sinovenous thrombosis, 1:6800 for NHS, and 1:65000 for presumed perinatal hemorrhagic stroke. The apparent birth prevalence of NAIS and NHS increased over time. There were more males affected than females. The age at diagnosis decreased for late-presenting stroke types. CONCLUSIONS: The estimated birth prevalence of term perinatal stroke is higher than previous estimates, which may be explained by population-based sampling of disease-specific states. This emphasizes the need for further studies to better understand the disease-specific pathophysiology to improve treatment and prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".