Expanding the clinical phenotype in patients with disease causing variants associated with atypical Usher syndrome
Bibliographic record
Abstract
Atypical Usher syndrome (USH) is poorly defined with a broad clinical spectrum. Here, we characterize the clinical phenotype of disease caused by variants in CEP78, CEP250, ARSG, and ABHD12.Chart review evaluating demographic, clinical, imaging, and genetic findings of 19 patients from 18 families with a clinical diagnosis of retinal disease and confirmed disease-causing variants in CEP78, CEP250, ARSG, or ABHD12.CEP78-related disease included sensorineural hearing loss (SNHL) in 6/7 patients and demonstrated a broad phenotypic spectrum including: vascular attenuation, pallor of the optic disc, intraretinal pigment, retinal pigment epithelium mottling, areas of mid-peripheral hypo-autofluorescence, outer retinal atrophy, mild pigmentary changes in the macula, foveal hypo-autofluorescence, and granularity of the ellipsoid zone. Nonsense and frameshift variants in CEP250 showed mild retinal disease with progressive, non-congenital SNHL. ARSG variants resulted in a characteristic pericentral pattern of hypo-autofluorescence with one patient reporting non-congenital SNHL. ABHD12-related disease showed rod-cone dystrophy with macular involvement, early and severe decreased best corrected visual acuity, and non-congenital SNHL ranging from unreported to severe.This study serves to expand the clinical phenotypes of atypical USH. Given the variable findings, atypical USH should be considered in patients with peripheral and macular retinal disease even without the typical RP phenotype especially when SNHL is noted. Additionally, genetic screening may be useful in patients who have clinical symptoms and retinal findings even in the absence of known SNHL given the variability of atypical USH.
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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".