Axenfeld-Rieger syndrome: more than meets the eye
Bibliographic record
Abstract
Background Axenfeld-Rieger syndrome (ARS) is characterised by typical anterior segment anomalies, with or without systemic features. The discovery of causative genes identified ARS subtypes with distinct phenotypes, but our understanding is incomplete, complicated by the rarity of the condition. Methods Genetic and phenotypic characterisation of the largest reported ARS cohort through comprehensive genetic and clinical data analyses. Results 128 individuals with causative variants in PITX2 or FOXC1, including 81 new cases, were investigated. Ocular anomalies showed significant overlap but with broader variability and earlier onset of glaucoma for FOXC1-related ARS. Systemic anomalies were seen in all individuals with PITX2-related ARS and the majority of those with FOXC1-related ARS. PITX2-related ARS demonstrated typical umbilical anomalies and dental microdontia/hypodontia/oligodontia, along with a novel high rate of Meckel diverticulum. FOXC1-related ARS exhibited characteristic hearing loss and congenital heart defects as well as previously unrecognised phenotypes of dental enamel hypoplasia and/or crowding, a range of skeletal and joint anomalies, hypotonia/early delay and feeding disorders with structural oesophageal anomalies in some. Brain imaging revealed highly penetrant white matter hyperintensities, colpocephaly/ventriculomegaly and frequent arachnoid cysts. The expanded phenotype of FOXC1-related ARS identified here was found to fully overlap features of De Hauwere syndrome. The results were used to generate gene-specific management plans for the two types of ARS. Conclusion Since clinical features of ARS vary significantly based on the affected gene, it is critical that families are provided with a gene-specific diagnosis, PITX2-related ARS or FOXC1-related ARS. De Hauwere syndrome is proposed to be a FOXC1opathy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".