Quantifying end‐stage electrophysiological function in progressive retinal degenerative disorders (PRDD)
Bibliographic record
Abstract
Abstract Purpose PRDD, such as Retinitis Pigmentosa, are accompanied with a gradual reduction of ERG signal to non‐measurable amplitudes. We compared alternative means of quantifying normal and pathological ERGs. Methods Photopic ERGs (DTL electrode, background 30 cd.m‐2; flash stimuli: ‐2.62 to 0.64 log cd.sec.m‐2 in 17 steps of ~ ‐0.2 log‐unit) were recorded from 85 normal subjects and 55 patients with PRDD. In a subset of 6 normal subjects, focal ERGs (fERGs) were obtained with the use of a eye patch to restrict the stimulus centrally and at 20o or 40o nasally. ERG descriptors, obtained with Direct Wavelet Transform D(WT) of the ERGs, were compared to the traditional amplitude measurements. Results In normal, the ERG amplitude gradually decreased from 131.42±31.27µV (Vmax) to 0.71±0.12 µV (dimmest flash used) in two distinct pseudo‐asymptotical steps of ‐15.2±2.0µV.s (step 1) and ‐0.42±0.1µV.s per decrement respectively (9 steps each). Pathological ERGs as well as normal focal ERGs could always be fitted to this model. Furthermore, while the traditional measurements frequently failed to quantify residual ERGs, including the normal fERGs, the DWT was always able to extract quantifiable and comparable information from the residual response, thus permitting a more favourable prognosis. Conclusion Analysis of the ERG response in the time and frequency domain (such as DWT) allows for a more precise quantification of the ERG signal especially when it reaches residual amplitudes such as that observed in end‐stage PRDD. Our results suggest that modeling ERG attenuation with the DWT improves the staging and prognosis of patients affected with severe PRDD. Supported by FFB (USA).
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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 | 0.001 | 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".