Development of Benign Enlargement of Subarachnoid Spaces Growth Charts
Bibliographic record
Abstract
Background: This study aimed to develop head circumference (HC) growth charts specific to infants and young children diagnosed with benign enlargement of the subarachnoid spaces. By modeling HC trajectories, we sought to establish normative curves that distinguish benign macrocephaly from pathologic causes of head enlargement. Our objective was to support accurate monitoring, reduce unnecessary imaging, and guide appropriate referral decisions in pediatric patients with macrocephaly. Study design: A retrospective analysis of HC measurements was conducted using data from 137 patients aged 0-6 years diagnosed with benign macrocephaly at the Children's Hospital of Eastern Ontario. Generalized Additive Models for Location, Scale, and Shape were employed to create growth charts. Results: The study included 137 children with neuroimaging confirming benign macrocephaly diagnoses. Exclusion criteria were applied to ensure the cohort was representative of isolated benign macrocephaly cases. Benign macrocephalic percentile charts for HC were generated, showing accelerated early growth followed by stabilization around ages 2-3. For both boys and girls, the 50th centile tracks consistently above the 97th in World Health Organization data for non-benign macrocephalic patients. The majority of benign macrocephalic patients fell above the 97.5th percentile of the World Health Organization reference curves. We identified pathological growth by examining HCs greater than 60 cm and assessing for rapidly progressive HC growth patterns that required surgical intervention. Conclusions: We provide novel benign macrocephaly growth charts that enable accurate monitoring of HC in clinical settings. Furthermore, the use of these curves may help differentiate patients with benign macrocephaly from other entities that may warrant surgical intervention such as hydrocephalus. Future research should validate these findings in multicenter prospective studies for practitioners to remain abreast in clinical management strategies for infants with benign macrocephaly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".