Detecting Early Functional Damage in Glaucoma Suspect and Ocular Hypertensive Patients With the Multifocal VEP Technique
Bibliographic record
Abstract
PURPOSE: To determine whether the multifocal visual evoked potential (mfVEP) technique can detect early functional damage in ocular hypertensive (OHT) and glaucoma suspect (GS) patients with normal standard achromatic automated perimetry (SAP) results. PATIENTS AND METHODS: Twenty-five GS patients (25 eyes), 25 patients with OHT (25 eyes), and 50 normal controls (50 eyes) were enrolled in this study. All GS, OHT and normal control eyes had normal SAP as defined by a pattern standard deviation and mean deviation within the 95% confidence interval and a glaucoma hemifield test within normal limits on the Humphrey visual field 24-2 program. Eyes with GS had optic disc changes consistent with glaucoma with or without raised intraocular pressure (IOP), and eyes with OHT showed no evidence of glaucomatous optic neuropathy and IOPs >or=22 mm Hg. Monocular mfVEPs were obtained from both eyes of each subject using a pattern-reversal dartboard array with 60 sectors. The entire display had a radius of 22.3 degrees. The mfVEPs, for each eye, were defined as abnormal when either the monocular or interocular probability plot had a cluster of 3 or more contiguous points with P<0.05 and at least 2 of these points with P<0.01. RESULTS: The mfVEP results were abnormal in 4% of the eyes from normal subjects. Abnormal mfVEPs were detected in 20% of the eyes of GS patients and 16% of the eyes of OHT patients. Significantly more mfVEP abnormalities were detected in GS patients than in normal controls. However, there was no significant difference in mfVEP results between OHT patients and normal controls. CONCLUSIONS: The mfVEP technique can detect visual field deficits in a minority of eyes with glaucomatous optic disks and normal SAP results.
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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 | 0.000 | 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) | 0.001 | 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".