Reliability of Handheld Optical Coherence Tomography in Children Younger Than Three Years of Age Undergoing Vigabatrin Treatment for Childhood Epilepsy
Bibliographic record
Abstract
Purpose: Vigabatrin-associated retinal toxicity manifests as reduction in the clinical electroretinogram and retinal nerve fiber layer (RNFL) thinning. This observational investigation of RNFL thickness in young vigabatrin-treated children was to identify intravisit and intervisit reliabilities of peripapillary RNFL thickness measurements performed with Envisu (optical coherence tomography) OCT. Secondarily, a longitudinal assessment investigated the presence and extent of RNFL thinning. Methods: We measured the handheld OCT in sedated children to evaluate the RNFL thickness using segmentation software. Intraclass correlation coefficient (ICC) statistics identified intravisit and intervisit reliabilities for RNFL thickness. Results: Twenty-nine children (10.1 ± 6.0 months old) underwent handheld optical coherence tomography (OCT). Fourteen of these completed follow-up assessments. Intravisit reliability was good for the right eye (ICCs = 0.82-0.98) and the left eye (ICCs = 0.75-0.89) for each of the 4 retinal quadrants. Inter-visit ICCs for each of the 4 retinal quadrants were good (ICC = 0.82-0.98). There was no consistent change in RNFL thickness longitudinally. Conclusions: In this pediatric cohort, RNFL thickness measures using handheld OCT provided good reliability within a single visit and between consecutive visits supporting its use as an adjunctive tool in the clinical setting. Further long-term follow-up is required to understand RNFL thickness changes in this specific population and its association with vigabatrin toxicity. Translational Relevance: The findings of good reliability and clinical feasibility would provide an opportunity for the handheld OCT to monitor reliably for vigabatrin-associated retinal toxicity in children who often show noncompliance to traditional testing approaches.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".