Congenital Stationary Night Blindness: Clinical and Genetic Features
Bibliographic record
Abstract
Congenital stationary night blindness (CSNB) is an inherited retinal disease (IRD) that causes night blindness in childhood with heterogeneous genetic, electrophysical, and clinical characteristics. The development of sequencing technologies and gene therapy have increased the ease and urgency of diagnosing IRDs. This study describes seven Taiwanese patients from six unrelated families examined at a tertiary referral center, diagnosed with CSNB, and confirmed by genetic testing. Complete ophthalmic exams included best corrected visual acuity, retinal imaging, and an electroretinogram. The effects of identified novel variants were predicted using clinical details, protein prediction tools, and conservation scores. One patient had an autosomal dominant CSNB with a RHO variant; five patients had complete CSNB with variants in GRM6, TRPM1, and NYX; and one patient had incomplete CSNB with variants in CACNA1F. The patients had Riggs and Schubert–Bornschein types of CSNB with autosomal dominant, autosomal recessive, and X-linked inheritance patterns. This is the first report of CSNB patients in Taiwan with confirmed genetic testing, providing novel perspectives on molecular etiology and genotype–phenotype correlation of CSNB. Particularly, variants in TRPM1, NYX, and CACNA1F in our patient cohort have not previously been described, although their clinical significance needs further study. Additional study is needed for the genotype–phenotype correlation of different mutations causing CSNB. In addition to genetic etiology, the future of gene therapy for CSNB patients is reviewed and discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".