PDCT-05. STEADY-STATE VISUAL EVOKED POTENTIALS TO BETTER ASSESS VISUAL FUNCTIONS IN CHILDREN WITH OPTIC PATHWAY GLIOMAS
Bibliographic record
Abstract
Optic Pathway Gliomas (OPG) represent 4-6% of brain tumors in children. Magnetic resonance imaging (MRI) and visual acuity are usually used to evaluate clinical evolution and treatment response. Since the goal of treatment is to preserve vision, it is important to develop new rapid, reliable, and non-invasive techniques to objectively measure the integrity visual functions in patients with optic pathway gliomas. Using a new approach with steady state visual evoked potentials (ssVEP), we were able to simultaneously assess central and peripheral visual fields of participants. Two circular dartboard patterns from fovea to peripheral zones up to 32 degrees of eccentricity are presented to the patients at two different flickering frequencies 14.4 and 16 reversal/s. Each stimulation is presented at two different levels of contrast (96 and 30%) to maximise sensitivity. To assess visual field integrity, 8 blocks of 10 seconds are presented to the participants. Since the initiation of this pilot study, 14 controls and 4 patients with optic pathway gliomas have been enrolled and followed prospectively. The preliminary results indicate that the method is promising for evaluating the integrity of the visual field in visually impaired populations (i.e. children with OPGs). The next step of our project is to use this technique to measure visual functions in children with OPGs aged 4 to 21 years prospectively over a period of 12 months. We will compare ssVEP obtained with MRIs and standard assessments in neuro-ophthalmology, which includes visual acuity and optical coherence tomography. This new electrophysiology method could potentially improve the monitoring and management of patients with OPGs by reliably detecting subtle visual changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".