Genetic Spectrum of Negative Electroretinograms in a Predominantly Pediatric Cohort of 177 Patients
Bibliographic record
Abstract
Purpose: The purpose of this study was to elucidate the common and rare genetic causes of negative electroretinograms (nERGs) and their association with systemic disease and with myopia in a predominantly pediatric cohort. Methods: Patients underwent electroretinogram (ERG) testing at the Hospital for Sick Children (Toronto) between 2007 and 2023. Negative ERG was defined as b/a amplitude of <1 to a dark-adapted 3.0 and/or 10.0 cd*s*m-2 stimulus. Genetic testing results were reanalyzed using American College of Medical Genetics guidelines. Genes accounting for <2.5% of cases were defined as rare. Results: Of 4347 ERGs performed, 293 (6.7%) cases had nERGs. Among these, 276 (94.1%) were classified as inherited; 193 had genetic testing. Of these, 177 (91.7%) had an established genetic diagnosis involving 41 genes. Major phenotypes included congenital stationary night blindness (CSNB, 55.4%), retinoschisis (18.6%), and photoreceptor dystrophies (17.5%). Both common (CACNA1F, RS1, TRPM1, NYX, and IDUA) and rare genetic associations were identified. Among patients with CSNB, postsynaptic ON-bipolar genes were associated with high myopia, increasing refractive error by -6.12 diopters (D). Collectively, rare causes affected the same number of cases as CACNA1F, the most frequently associated gene (27.7% each). Genetic etiology varied (23 genes; 31 cases) among photoreceptor dystrophies causing nERG. Systemic disease affected 31 cases. Five novel genetic associations (ABHD12, AP3B2, OAT, PCDH15, and PDE6A) were found. Conclusions: This study expands the genetic spectrum underlying nERGs, confirming prior associations and identifying five novel genes associations. We provide evidence for the role of ON-bipolar pathway in myopia development. In photoreceptor dystrophies, nERGs rarely occur and likely represent a transient epiphenomenon independent of the underlying gene or mechanism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".