A Primer for Predicting Risk of Disease in <i>HFE</i> -Linked Hemochromatosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it