Bibliographic record
Abstract
Introduction Alagille syndrome (ALGS) is an autosomal dominant, multisystem disorder which was first described in 1969 by Daniel Alagille as a constellation of clinical features in five different organ systems [1]. The diagnosis was based on the presence of intrahepatic bile duct paucity on liver biopsy in association with at least three of the major clinical features: chronic cholestasis, cardiac disease (most often peripheral pulmonary stenosis), skeletal abnormalities (typically butterfly vertebrae), ocular abnormalities (primarily posterior embryotoxon), and characteristic facial features. Advances in molecular diagnostics have enabled an appreciation of the broader disease phenotype with recognition of renal and vascular involvement [2,3]. There is significant variability in the extent to which each of these systems is affected in an individual, if at all [4,5]. It was originally estimated that ALGS had a frequency of 1 in 70000 live births, although this was based on the presence of neonatal cholestasis. However, this is clearly an underestimate as molecular testing has demonstrated that many individuals with a disease-causing mutation do not have neonatal liver disease and the true frequency is likely closer to 1 in 30000 [5]. Alagille syndrome is caused by mutations in JAGGED1 (JAG1) , encoding a ligand Jagged1 in the Notch signaling pathway [6,7]. Mutations in JAG1 are identified in 94% of clinically defined probands [8]. Recently, mutations in NOTCH2 have been identified in a few patients with ALGS who do not have JAG1 mutations [9]. This exciting development has enhanced our understanding of the heterogeneity of this disorder, although much remains to be understood about the tremendous variability seen in affected individuals and the likely genetic modifiers involved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.016 |
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".