Bibliographic record
Abstract
We thank Drs Singh and Mondia for their interest in our work and their comments regarding our article titled “Natural history of liver disease in a large international Cohort of children with Alagille syndrome: results from The GALA Study.” We acknowledge the absence of detailed analyses of extrahepatic manifestations in our paper. Since Alagille syndrome (ALGS) is dominated by cholestatic liver disease, the focus of our first manuscript was to describe the natural history of liver disease in the largest real-world cohort of ALGS. Additional analyses into other organ systems were simply beyond the scope of this paper. However, we absolutely agree that as Hepatologists/Gastroenterologists, we must think beyond the liver, and through the rich and robust Global Alagille Alliance (GALA) data set, these analyses are finally possible. The suggested analyses are currently planned or in progress and will allow us to coordinate subspecialist care and provide evidenced-based holistic care. We look forward to sharing these results in upcoming publications. CONFLICT OF INTEREST Binita M. Kamath consults for and received grants from Mirum and Albireo. She consults for Audentes. Shannon M. Vandriel has no conflicts to report.
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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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.019 | 0.033 |
| Insufficient payload (model declined to judge) | 0.029 | 0.024 |
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".