P.187 Development of benign enlargement of subarachnoid spaces growth charts
Bibliographic record
Abstract
Background: Normalized growth curves are an essential component in management of pediatric patients. Benign enlargement of subarachnoid spaces (BESS) is a common condition in infants that results in deviation from expected head growth but does not have long term implications. Differentiating BESS from pathological conditions is critical to minimize unnecessary imaging and specialist evaluations. Standardized growth charts specific to BESS do not exist, complicating monitoring and management. Methods: An analysis of head circumference (HC) data was performed for 315 children aged 0-6 years diagnosed with BESS at CHEO. Growth charts were created using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Z-scores derived from HC measurements were compared to World Health Organization (WHO) norms, stratified by sex. Results: Benign macrocephalic patients consistently tracked above the 97th percentile of WHO curves, with the 50th percentile in this cohort aligning with the 97th percentile of WHO data. HC growth accelerated in early infancy, stabilizing around ages 2-3. Growth charts demonstrated distinct patterns for BESS compared to normative data. Conclusions: This study provides novel charts for BESS, enabling improved monitoring and clinical management. These charts have the potential to reduce unnecessary imaging and specialist referrals, alleviating anxiety for caregivers and clinicians while optimizing resource use.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".