Mathematical analysis of the cone ERG photopic hill: Clinical applications
Bibliographic record
Abstract
Abstract Purpose: With brighter stimuli, the photopic ERG b‐wave increases to a maximal value and then decreases to a plateau, a feature known as the Photopic Hill (PH). Recently, a mathematical model combining a Gaussian (GF) and a Logistic Growth (LGF) functions was developed to fit the PH (Hamilton et al., Vision Research, in press). We examined if this equation could help us sort out selected retinopathies. Methods: We compared PHs (background: 30 cd.m‐2; intensities: ‐0.8 to 2.84 log cd.sec.m‐2) obtained from normals (N=40) and patients (N=20) affected with Congenital Stationary Night Blindness (CSNB), Congenital Postreceptoral Cone Pathway Anomaly (CPCPA) and Retinitis Pigmentosa (RP) with the GL ratio [GL= Gb / (Gb+Vbmax)] were Gb and Vbmax represent the amplitude of the Gaussian and logistic (Vbmax) functions respectively. Results: The normal GL ratio is 0.60 ± 0.08 (mean ± 1SD) compared to ≈1.0 in CSNB (almost pure GF) and 0.32±0.08 in CPCP [reduced GF (p<.05) and normal LF (p>.05)] patients. Six of the 8 RP patients had a GL ratio above 0.5 (mean GL= 0.70 ± 0.19) and 2 below (0.28 and 0.41). Of interest, while in some retinopathies, a decline in Gb and Vbmax occurred with disease progression (longitudinal and transversal comparisons), it did not always modify the GL ratio. Conclusions: Human PH can be dissected into two distinct and concomitant phenomena each represented by its own equation. Altghough the retinal origin of the GF and LGF awaits to be confirmed, use of this mathematical approach appears to add valuable information that will further refine the diagnosis of retinal disorders affecting the photopic (cone) pathway. Supported by CIHR and Réseau Vision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".