The role of Frizzled-4 mutations in familial exudative vitreoretinopathy and Coats disease
Bibliographic record
Abstract
AIM: The aim of this study is to assess the role of Frizzled-4 (FZD4) in familial exudative vitreoretinopathy (FEVR) and Coats disease. METHODS: Tissue samples were collected for DNA extraction and automated DNA sequencing of the two coding exons of FZD4 in both directions. Cases carrying a FZD4 mutation and demonstrating extreme disease severity were selected for direct automated sequencing of all coding exons of LRP5, NDP and TSPAN12. Clinical data were obtained for the purpose of identifying genotype-phenotype correlations. RESULTS: 68 probands were diagnosed as having autosomal dominant or sporadic FEVR. Eleven FZD4 mutations (five missense, three deletions, one insertion, two nonsense) were identified. Six of these mutations are novel, and none were found in 346 control chromosomes. In 16 cases of Coats disease, one polymorphism combination was found in two samples: no mutations were detected. No genotype-phenotype correlation emerged. Three severely affected cases with FZD4 mutations failed to show additional mutations in the three other FEVR genes. CONCLUSION: The authors identified 12 FEVR probands with FZD4 mutations. FZD4 mutation screening can be a useful tool especially in mild or atypical cases of FEVR. Germ-line mutations in FZD4 do not appear to be a common cause of Coats disease.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".