Abstract TP159: Association Of Lesion Characteristics And Neurological Outcomes In Neonatal Arterial Ischemic Stroke Patients
Bibliographic record
Abstract
Background: Neonatal arterial ischemic stroke (NAIS) occurs as a result of an acute focal cerebral arterial infarction between birth and 28 days of life in nearly 1:2500 live births. As there are few studies examining the relationship between NAIS lesion characteristics and clinical outcomes, this study aims to determine if lesion volume and lesion location are associated with abnormal neurological outcomes in NAIS. Methods: Semi-automated lesion segmentation of co-registered multi-modal images was conducted using ITK-SNAP. Semi-automated lesion segmentations with manual corrections were performed using ITK-SNAP software. Lesion volumes were corrected for head size by dividing lesion volumes by total intracranial volume, and correlated (using Kendall's Tau-b [τ b ]) with neurological outcome that was assessed with the Pediatric Stroke Outcome Measure (PSOM). Results: Participants included N=20 NAIS patients (mean [M] weeks gestation=39.5, +/-1.64; 55% male, 70% single infarct, 75% left-sided lesions). Outcome was assessed an average of 312, +/-7.6 days post-stroke. Neurological deficits were present in 50% (with seizures in 85%) at time of stroke, and infarct locations most commonly involved supratentorial cortical structures (75%), however none were correlated with outcome. Larger lesion volumes (M=5.35 mm 3 , +/-0.069) were correlated with poor outcome (τ b =.444, p=0.02). Conclusion: We found larger lesion volume to be associated with atypical clinical outcomes following NAIS. Further analysis is in progress that include changes over time in lesion characteristics and outcome. These results demonstrate a need for better characterize the relationship between lesion characteristics and outcomes in NAIS.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".