Update of PAX2 mutations in renal coloboma syndrome and establishment of a locus-specific database
Bibliographic record
Abstract
Renal coloboma syndrome, also known as papillorenal syndrome is an autosomal-dominant disorder characterized by ocular and renal malformations. Mutations in the paired-box gene, PAX2, have been identified in approximately half of individuals with classic findings of renal hypoplasia/dysplasia and abnormalities of the optic nerve. Prior to 2011, there was no actively maintained locus-specific database (LSDB) cataloguing the extent of genetic variation in the PAX2 gene and phenotypic variation in individuals with renal coloboma syndrome. Review of published cases and the collective diagnostic experience of three laboratories in the United States, France, and New Zealand identified 55 unique mutations in 173 individuals from 86 families. The three clinical laboratories participating in this collaboration contributed 28 novel variations in 68 individuals in 33 families, which represent a 50% increase in the number of variations, patients, and families published in the medical literature. An LSDB was created using the Leiden Open Variation Database platform: www.lovd.nl/PAX2. The most common findings reported in this series were abnormal renal structure or function (92% of individuals), ophthalmological abnormalities (77% of individuals), and hearing loss (7% of individuals). Additional clinical findings and genetic counseling implications are 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".