Improving the diagnostic potential of the photopic electroretinogram (ERG) with refined mathematical tools
Bibliographic record
Abstract
Abstract Purpose Examine if the use of refined mathematical tools increases the understanding and diagnostic potential of the photopic ERGs. Methods Analyses were performed on normal and pathological photopic ERGs (background of 30 cd.m‐2; flash intensities ranging from ‐0.8 to 2.84 log cd.sec.m‐2 in 15 steps; n>100). Mathematical instruments included: 1‐ the Continuous Wavelet Transform (CWT)2‐ The Reiman Integrator and 3‐ the Photopic Hill Gaussian:Logistic ratio [GL=Gb/(Gb+Vbmax)]claimed to weight the contribution of the OFF and ON pathways to the photopic ERG. Results 1‐ CWT revealed 3 distinct frequency domains within the 10‐50 msec poststimulus time interval, namely: 20‐40 Hz, 90‐150 Hz and 200‐300 Hz that appeared to be independently modulated by flash intensity and/or pathology. 2‐ Integration of the OPs accurately reconstructed the broadband ERGs (r>0.90), irrespective of CWT. 3‐ In our cohort of RP patients (n=50), the GL ratio was 0.43 ±0.23, compared to 0.60±0.08 in normal (p<.05). Conclusion 1‐CWT dissects the ERG into its primary components, a method that should allow a more accurate quantification of ERG responses. 2‐ Strong evidence supports the concept that the b‐wave results from the integration of the OPs. The value of the integration constant (1 sec) is close to the time constant of the Muller cell membrane suggesting that the latter could be at the origin of this integration. 3‐The broader GL distribution obtained from our RP cohort suggests that in some the OFF retinal pathway is primarily affected while in others the ON is. It remains to be determined if the latter represents two stages of the same disease process or two different disease paths. 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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".