Genotype-Phenotype Characterization of Novel Variants in Six Italian Patients with Familial Exudative Vitreoretinopathy
Bibliographic record
Abstract
Familial exudative vitreoretinopathy (FEVR) is a complex disorder characterized by incomplete development of the retinal vasculature. Here, we report the results obtained on the spectrum of genetic variations and correlated phenotypes found in a cohort of Italian FEVR patients. Eight probands (age range 7–19 years) were assessed by genetic analysis and comprehensive age-appropriate ophthalmic examination. Genetic testing investigated the genes most widely associated in literature with FEVR: FZD4 , LRP5 , TSPAN12 , and NDP . Clinical and genetic evaluations were extended to relatives of probands positive to genetic testing. Six out of eight probands (75%) showed a genetic variation probably related to the phenotype. We identified four novel genetic variants, one variant already described in association with Norrie disease and one previously described linked to autosomal dominant FEVR. Pedigree analysis of patients led to the classification of four autosomal dominant cases of FEVR (caused by FZD4 and TSPAN12 variants) and two X-linked FEVR probands ( NDP variants). None of the patients showed variants in the LRP5 gene. This study represents the largest cohort study in Italian FEVR patients. Our findings are in agreement with the previous literature confirming that FEVR is a clinically and genetically heterogeneous retinal disorder, even when it manifests in the same family.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".