A.02 Assessing visual functions in children with an optic pathway glioma using steady-state visual evoked potentials
Bibliographic record
Abstract
Background: Optic pathway gliomas (OPG) represent 5% of pediatric brain tumours. Visual acuity measures are used to evaluate treatment response. Current clinical tests to assess visual field integrity are subjective and require verbal cooperation. Thus, the objective of this study was to evaluate the clinical effectiveness of Steady State Visual Evoked Potentials (ssVEPs) to measure visual field integrity in a non-invasive and objective manner. Methods: SsVEPs were obtained in ten children with OPGs and 42 controls ages 3 to 21. The stimuli consisted of two circular dartboard patterns stimulating fovea and peripheral zones at two flickering frequencies, so that central and peripheral visual fields could be assessed simultaneously. The test consisted of eight stimuli presentations of 10 seconds. Results: Results indicate significantly lower ssVEP amplitudes in children with OPGs (M=2.52, 95% CI [1.13, 3.92]) compared to controls (M=13.26, 95% CI [8.85, 17.67]) in the central visual field (p=.021). However, no between group differences were detected in the peripheral field (p >.05). There were no significant differences between age groups (p>0.05). Conclusions: This objective, affordable, and non-invasive method appears to be effective in detecting central visual field deficits in children with OPGs rapidly and consistently.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".