B.2 Neurologic outcome trajectory following neonatal arterial ischemic stroke (NAIS): A longitudinal observational study
Bibliographic record
Abstract
Background: Studies evaluating long-term neurologic outcomes following NAIS are scanty. We aimed to study the emergence pattern of neurologic deficits following NAIS. Methods: Neonates diagnosed with AIS were prospectively enrolled and outcomes were evaluated using the validated Pediatric Stroke Outcome Measure-Severity Classification Scheme. Neurologic outcomes were classified as normal/mild, moderate or severe. Trend analysis was conducted using Cochran-Armitage test. Results: A total of 126 neonates (59% males) were followed for a median of 5.2 years (IQR:3.4-6.4 years). The proportion of children classified as normal/mild declined from 94% to 76% >5 years post-stroke (p<0.01). Moderate and severe outcomes increased from 5% to 15% and 1% to 8% (p=0.01), respectively. Sensorimotor, language and cognitive deficits emerged in 16%, 14%, and 17% of enrolled neonates, respectively. Of those who had normal/mild outcomes at baseline, 83 remained stable throughout the study. Improvement in neurologic outcomes was seen in 8 children. Thirty-five neonates had emerging deficits at one point during follow-up. Congenital heart disease predicted the emergence of deficits (odds ratio=3.3, 95% confidence interval:1.01-10.5). Conclusions: Emerging deficits following NAIS are not uncommon and can equally manifest in sensorimotor, language or cognitive domains. Thus, long-term follow-up and close monitoring of outcomes following NAIS is crucial.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".