Placental Pathology in Neonatal Stroke
Bibliographic record
Abstract
OBJECTIVE: Neonatal stroke is increasingly recognized, and risk factors have been identified. The placenta has been implicated as a potential contributor to neonatal stroke; however, pathology has not been previously described. This case series systematically evaluates prenatal, maternal, and neonatal risk factors and describes placental pathology in 12 cases of neonatal stroke. PATIENTS AND METHODS: We reviewed the Canadian Pediatric Ischemic Stroke Registry from 1992 to 2006, which consists of 186 neonatal stroke patients. Twelve patients with symptomatic cerebral arterial ischemic stroke or sinovenous thrombosis had their placenta available for pathologic examination. Clinical presentation; maternal, prenatal, and neonatal risk factors for stroke; and patient outcome were collected retrospectively from patient charts. Gross and microscopic placental pathology was described and classified into 4 pathologic categories. RESULTS: Of 12 patients studied, 10 patients were male, 5 patients had arterial ischemic stroke, and 7 patients had sinovenous thrombosis. Maternal risk factors were identified in 5 cases, prenatal risk factors in 10 cases, and neonatal risk factors in 10 cases. Placental lesions were present in 10 cases and were classified as thromboinflammatory process in 6 cases, sudden catastrophic event in 5 cases, decreased placental reserve in 3 cases, and stressful intrauterine environment in 2 cases. CONCLUSIONS: This study reviews detailed placental pathology in a selected cohort of patients presenting near the time of delivery and correlates this with clinical presentation, outcome, and risk factors for neonatal stroke. Our results suggest that multiple risk factors are involved in neonatal stroke, and placental pathology may be a contributing factor. The implications of specific placental lesions remain to be determined with larger, case-controlled studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".