Abstract TP507: Sex-Related Differences in Neonatal Stroke: International Maternal Newborn Stroke Registry
Bibliographic record
Abstract
Background: There is evidence that male neonates have higher incidences of ischemic stroke and associated limitations in brain repair compared to female neonates. We used data from the International Maternal Newborn Stroke Registry (IMNSR) to further explore neonatal sex differences in demographics, birth characteristics, stroke onset, and maternal factors. Methods: Eleven international sites participated; 8 entered data for this analysis. Eligible participants with newborn (28 weeks gestation to 28 postnatal days) ischemic or hemorrhagic stroke or cerebral venous thrombosis were identified prospectively and retrospectively and enrolled. Preterm infants born before 28 weeks and neonates with germinal matrix intraventricular hemorrhage were excluded. We collected APGAR scores at 1 and 5 minutes, resuscitation status, demographics, stroke type, maternal and gestational age at birth, and mother’s health conditions at birth. Descriptive statistics were performed to identify differences in female versus male neonate cases related to demographics, pregnancy-related factors, and birth-related factors. Results: We analyzed 68 cases, 38 males and 30 females. Median maternal age for all cases was 31 y (IQR 29-34). Compared to females, males were more likely to have a lower APGAR 1 score and receive resuscitation, and more likely to have a stroke onset within the first week of life. This data set did not show significant gender-related differences in stroke type, or maternal health concerns during pregnancy (Table). Conclusions: We found that males with newborn strokes had more complications at birth and were more likely to have strokes present in the first week of life than females. Further research on the reasons for these sex differences is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".