Utility of Light-Adapted Full-Field Electroretinogram ON and OFF Responses for Detecting Glaucomatous Functional Damage
Bibliographic record
Abstract
Purpose: To compare parameters of electroretinogram (ERG) responses for their ability to detect functional loss in early stages of nonhuman primate (NHP) experimental glaucoma (EG), including photopic negative responses (PhNR) to a standard brief red flash on a blue background (R/B) and 200-ms-long R/B and white-on-white (W/W) flashes, to W/W flicker stimuli (5-50 Hz), and to a dark-adapted intensity series. Methods: Light-adapted ERGs were recorded in 12 anesthetized monkeys with unilateral EG. Amplitudes and implicit times of the a-wave, b-wave, and d-wave were measured, as well as amplitudes of PhNRs and oscillatory potentials for flash onset and offset. Flicker ERGs were measured using peak-trough and fundamental frequency analyses. Dark-adapted ERG parameters were modeled by Naka-Rushton relationships. Results: Only PhNR amplitudes were significantly reduced in EG eyes compared to fellow control (FC) eyes. The d-wave implicit time was delayed in EG versus FC eyes only for the W/W long flash, but in all eyes it was 10 to 20 ms slower for R/B versus the W/W condition. Flicker ERGs were <0.5 ms delayed in EG versus FC overall, but amplitudes were affected only at 5 Hz. The brief R/B PhNR amplitude had the highest sensitivity to detect EG and strongest correlation to parameters of structural damage. Conclusions: The PhNR to the standard brief R/B stimulus was best for detecting and following early-stage functional loss in NHP EG. Translational Relevance: These results suggest that there would be no benefit in using longer duration flashes to separate onset and offset responses for clinical management of glaucoma.
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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 | 0.001 |
| 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 | 0.000 | 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".