Neonatal hemorrhagic stroke: population-based epidemology
Bibliographic record
Abstract
Background: Stroke is a leading cause of perinatal brain injury and cerebral palsy. Term neonatal hemorrhagic stroke (NHS) is a common syndrome with poorly defined epidemiology. We aimed to determine incidence and mechanisms within a large population-based NHS sample. Methods: The Alberta Perinatal Stroke Project (APSP), a provincial registry ascertained NHS cases using exhaustive ICD-9/10 code searching (1992-2012, >2400 chart reviews). Prospective cases were captured through the Calgary Pediatric Stroke Program from 2007-2014 (n=387). All NHS cases underwent structured chart review using a data capture form and blinded review of neuroimaging. Provincial live births were obtained from statistics Canada. Outcomes were extrapolated to the Pediatric Stroke Outcome Measure (PSOM). Results: We identified 74 cases: 49 NHS (26 retrospective, 27 prospective), 4 presumed perinatal HS (PPHS), and 21 hemorrhagic transformation (HT) of ischemic injury. Incidence of NHS was 1:8800 live births (1:5820 for all forms). HT was common (28.4%) including global, arterial venous ischemic lesions. Presumed perinatal hemorrhagic stroke presented with epilepsy. No risk factor was identified in 68% of cases. Outcomes were abnormal (PSOM 1 or more) in 30% and better in the HT group. Conclusion: NHS occurs in 1:8800 live births. Imaging classification is essential to define mechanisms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".