PATHOGNOMONIC (DIAGNOSTIC) ERGs A Review and Update
Bibliographic record
Abstract
In Brief Purpose: To review three inherited retinal disorders associated with diagnostic or pathognomonic electroretinogram (ERG) abnormalities: cone dystrophy with supernormal rod ERG (KCNV2), enhanced S-cone syndrome (NR2E3), and bradyopsia (RGS9/R9AP). Methods: A review of clinical details, genetic basis, and electrophysiological features in these disorders and a brief summary of the standard and nonstandard ERG techniques required to identify the disorders. Results: The electrophysiological features in each of these three disorders are pathognomonic such that the responsible gene can be specified. The results from nonstandard electrophysiological testing in excess of international standards are necessary to describe the pathognomonic changes in cone dystrophy with supernormal rod ERG and bradyopsia. The clinical phenotype in the disorders can be variable. Mutations in NR2E3 may additionally be associated with phenotypes other than enhanced S-cone syndrome. Conclusion: Characteristic ERG changes enable the diagnosis of cone dystrophy with supernormal rod ERG, enhanced S-cone syndrome, and bradyopsia and accurate genetic screening. This review highlights the need for additional nonstandard ERGs to make the diagnosis in two of these disorders. Pathognomonic electroretinogram abnormalities, that is, changes that are specific for both a diagnosis and the responsible genetic defect, are found in only three inherited retinal disorders: “cone dystrophy with supernormal rod electroretinogram” (KCNV2), “enhanced S-cone syndrome” (NR2E3), and “bradyopsia” (RGS9/R9AP). Those disorders are reviewed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".