Neuroimaging Markers of Cognitive Outcome in Children with Perinatal Stroke (I2.002)
Bibliographic record
Abstract
Objective: Our objective is to determine if features seen on chronic MR imaging could be used to assist in prognosis of cognitive outcomes in children. Background: Perinatal ischemic strokes are caused by a vascular disruption between 20 weeks gestation and 28 days postnatal, resulting in a focal brain injury. Although studies show children with a perinatal stroke may have IQ in the normal range, there are individuals that have significant intellectual impairment. In literature symptomatic epilepsy has been linked to poor cognitive outcomes but evidence for other predictive factors is scant and inconsistent. Methods: Twenty-six children with perinatal stroke were tested with a neuropsychological battery including Wechsler IQ and additional tests of memory and spatial skills. Existing structural MRI were obtained for all patients. MRI were analyzed for location of stroke (by lobe and regions of interest), tissue volumes relative to healthy controls (based on curve estimation after automated segmentation), and hippocampus volumes (by manual tracing) and related to performance on neuropsychological measures. Results: The only connection between location of stroke lesion and testing performance that was found was a difference in FSIQ between those children that had a lesion that included the dorsolateral prefrontal (DLPF) cortex and those whose lesion did not. There was a correlation between grey matter volume loss and the verbal comprehension component of IQ testing. A reduction in hippocampus volume was observed in those children that had symptomatic epilepsy, but there was no correlation between volume and IQ or memory performance. Conclusions: As seen in other studies, prognosis of cognitive outcome is difficult and there was no correlation between site of lesion and areas of intellectual deficit. However, there was some indication that loss of more cortical volume, or involvement of the DLPF cortex may be risk factors for intellectual impairment in childhood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".