Clinical Prediction of Perinatal Arterial Ischemic Stroke
Bibliographic record
Abstract
Perinatal stroke is a well-defined but heterogenous group of disorders involving a focal disruption of cerebral blood flow between 20 weeks gestation and 28 days of life. At a combined incidence of 1:1000 live births, stroke in the perinatal period is more common than at any other time in childhood. Morbidity of perinatal stroke is high, and it is the most common cause of hemiparetic cerebral palsy. Years living with disability are amplified with deficits lasting a lifetime. Perinatal arterial ischemic stroke (PAIS) is the most common type of perinatal stroke. Advances in neuroimaging have allowed for exceptional growth in stroke diagnosis. However, etiology is poorly understood. Many pregnancy, delivery, and fetal risk factors have been considered, but targeted treatment and prevention efforts are still not possible. This thesis reviewed perinatal stroke and developed a diagnostic risk prediction model for PAIS. Pathophysiology, strategies for diagnosis, investigations, management, and outcomes were broken down by perinatal stroke disease, with an additional focus on family mental health and active trials for acute intervention. A diagnostic prediction model was then developed using novel, multisource data and multivariable logistic regression. Clinical pregnancy, delivery, and neonatal risk factors were collected from four registries including the Alberta Perinatal Stroke Project, Canadian Cerebral Palsy Registry, International Pediatric Stroke Study, and Alberta Pregnancy Outcomes and Nutrition study. Variable selection was based on peer-reviewed literature. The final model included nine clinical factors – maternal age, tobacco exposure, substance exposure, pre-eclampsia, chorioamnionitis, intrapartum maternal fever, emergency c-section, low 5-minute Apgar score, and male sex – to predict the risk of PAIS in a term neonate with good discrimination between cases and controls (C-statistic 0.73). This work highlights the lifelong effects of perinatal stroke on patients and families, and the potential for early perinatal stroke diagnosis. Findings suggest that clinical prediction and early, accurate diagnosis of PAIS may be possible using common clinical variables. Future research is needed to optimize risk prediction by better understanding perinatal stroke pathophysiology, including the role of the placenta, and identifying high-risk groups.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".