A Primer for Predicting Risk of Disease in <i>HFE</i> -Linked Hemochromatosis
Bibliographic record
Abstract
Since the discovery of the hemochromatosis gene (HFE) in 1996, there has been increasing interest in diagnostic testing for the C282Y and H63D mutations. The high frequency of these two alleles and their incomplete penetrance in homozygotes and compound heterozygotes make genetic counseling for hemochromatosis different from some other autosomal recessive conditions in that parents and children may also be at risk for iron overload, while homozygotes may remain asymptomatic. We provide a guideline for genetic counseling in HFE-linked hemochromatosis based on the genetic probability of inheriting HFE mutations and known information about expression of iron overload in various HFE genotypes. Genetic probabilities were based on allele frequencies derived from large population studies and Hardy-Weinberg equilibrium estimates. Expression of iron overload in those of various genotypes was based on available estimates of serum ferritin from population screening studies. Estimates for the likelihood of clinical iron overload requiring follow-up screening or treatment are provided by gender and genotype. The probability of inheriting HFE mutations and developing iron overload can be estimated in family members of a proband with HFE mutations. Many C282Y homozygotes will not have clinical iron overload. The risk is highest in men and their C282Y homozygous brothers and significantly lower in homozygous women. Iron overload is uncommon in compound heterozygotes and H63D homozygotes.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".