Additional file 1 of Non-coding cis-regulatory variants in HK1 cause congenital hyperinsulinism with variable disease severity
Bibliographic record
Abstract
Additional file 1: Table S1. Details of PCR primer sequences and methodology for Sanger sequencing. Table S2. Details of primer and reporter sequences and methodology for deletion screening. Table S3. Classification of HK1 variants identified according to current guidelines. Table S4. Individual-level clinical and genetic characteristics for 90 individuals with a clinical diagnosis of congenital hyperinsulinism (HI) and a monoallelic pathogenic or likely pathogenic variant within the HK1 regulatory region. Table S5. Individual-level clinical and genetic characteristics for 30 individuals with a clinical diagnosis of congenital hyperinsulinism (HI) and a monoallelic variant of uncertain significance within the HK1 regulatory region. Table S6. Summary of clinical features for 30 individuals with a clinical diagnosis of congenital hyperinsulinism and a monoallelic variant of uncertain significance within the HK1 regulatory region. Fig S1. Bar chart comparing the frequency of HK1 variants to other known genetic causes of hyperinsulinism in the Exeter cohort (n=2,949). Fig S2. Pedigrees depicting inheritance of HK1 variants of uncertain significance. Fig S3. Kaplan Meier plot showing the probability of congenital hyperinsulinism by age in individuals with a monoallelic pathogenic or likely pathogenic HK1 variant.
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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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.873 | 0.116 |
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".