Abstract WMP120: The Role of Age, Lesion Location and Volume in Predicting Long-Term Neurological Outcomes in Pediatric Ischemic Stroke
Bibliographic record
Abstract
Introduction: The balance of plasticity versus vulnerability to acute ischemic injury in the immature brain across development remains controversial. We aimed to investigate the relationship between age at stroke and lesion location and volume with long-term outcomes following arterial ischaemic stroke (AIS). Hypothesis: Age at stroke onset interacts with lesion location and volume to predict long-term neurological outcomes. Methods: In a single-centre prospective study, children aged term birth to 18 years with acute symptomatic AIS from 1992 to 2016 who underwent neuroimaging (CT/MRI) within 14 days of stroke onset were studied. Long term outcomes were assessed with the Pediatric Stroke Outcome Measure (PSOM). Outcomes were defined as normal (score 0-0.5 on 1-5 PSOM subscales), otherwise abnormal. AIS lesions were classified based on major (anterior, middle, posterior, other) cerebral arteries and branch sub-territories (e.g. MCA-lateral lenticulostriate). For each vascular territory, the proportion of infarcted brain was estimated as ‘small’ (≤50% territory) or ‘large’ (>50% territory). Factors predictive of long term outcomes were evaluated using logistic regression models. Results: Among 285 children, median age at stroke onset was 22 months (IQR: 0.03-101), 41% were females. PSOM scoring at median 9 years (IQR: 4-15) post-stroke was normal in 61%. Abnormal outcome was associated with age 1-4 years (OR=2.2, 95% CI: 1.02-4.9), ‘large’ proportion infarcts involving cortex within any major artery territory (OR=6.1, 95% CI: 2.6-14.4), and subcortical infarcts: OR= 2.7 (95% CI: 1.4-5.4) for small proportion infarcts; OR=8.3 (95% CI: 4.2-16.6) for large proportion infarcts in either medial or lateral lenticulostriate arteries; OR=15.8 (95% CI: 5-50.3) for large volume lesions in both lenticulostriate territories. In multivariate logistic regression, age, cortical and subcortical infarcts predicted long-term neurologic outcomes. Conclusions: In addition to age at stroke onset, infarct location including volume plays a key role in predicting long-term neurological outcomes in children. While the relationship between age and neurological outcomes seems to be bimodal, a linear effect of lesion volume on stroke outcomes is evident.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".