Distorted Optic Nerve Portends Neurological Complications in Infants With External Hydrocephalus
Bibliographic record
Abstract
Background: Benign external hydrocephalus (BEH) is defined by rapid increase in head circumference in infancy, with neuroimaging evidence of enlarged cerebrospinal fluid (CSF) spaces. BEH was postulated to predispose to subdural hematoma, neurocognitive impairments, and autism. There is currently no consensus on BEH diagnostic criteria and no biomarkers to predict neurological sequalae. Methods: MRI-based quantitative approach was used for measurement of potential imaging markers related to external hydrocephalus and their association with neurological outcomes. We scanned 23 infants diagnosed with BEH and 11 age-similar controls. Using anatomical measurements from a large sample of healthy infants (n = 150), Z-scores were calculated to classify subject's CSF spaces as enlarged (≥1.96SD of mean values) or normal. Results: Subjects with abnormally enlarged CSF spaces had a significantly wider and longer ON (p = 0.017 and p = 0.020, respectively), and a significantly less tortuous ON (p = 0.006). ON deformity demonstrated a high diagnostic accuracy for abnormally enlarged frontal subarachnoid space (AUC = 0.826) and interhemispheric fissure (AUC = 0.833). No significant association found between enlarged CSF spaces and neurological complications (OR = 0.330, 95%CI 0.070–1.553, p = 0.161). However, cluster analysis identified a distinct subgroup of children (23/34, 67.6%) with enlarged CSF spaces and a wider, longer and less tortuous ON, to have an increased risk for neurological complications (RR = 7.28, 95%CI 1.07–49.40). Discussion: This is the first report on the association between external hydrocephalus, ON deformity and neurological complications. Our findings challenge the current view of external hydrocephalus as a benign condition. ON deformity is a potential auxiliary marker for risk stratification in patients with enlarged CSF spaces.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".