Predicting<i>C282Y</i>Homozygote Genotype for Hemochromatosis Using Serum Ferritin and Transferrin Saturation Values from 44,809 Participants of the HEIRS Study
Bibliographic record
Abstract
INTRODUCTION: The simultaneous interpretation of serum ferritin level and transferrin saturation has been used as a clinical guide to diagnose genetic hemochromatosis. The Hemochromatosis and Iron Overload Screening (HEIRS) Study screened 101,168 North American participants for serum ferritin level and transferrin saturation, and C282Y genotyping for the HFE gene. METHODS: Logistic regression involving a subsample of Caucasians (n=44,809) was used to predict individual probabilities of HFE C282Y homozygosity using serum ferritin and transferrin saturation values. Men (n=17,323) and women (n=27,486) were analyzed separately. Regression equations were evaluated using area under the curve from ROC analysis and variance explained by Nagelkerke's pseudo R-squared. An Android smartphone App and website application were developed to provide physicians with easy access to predicting C282Y homozygosity of the HFE gene. RESULTS: The logistic equation had an area under the ROC curve of 0.91 for men and 0.89 for women. The pseudo R-squared was 0.44 for men and 0.34 for women. An example analysis was a Caucasian man with a transferrin saturation of 50% and a ferritin level of 500 µg⁄L, who had a 1.3% (95% CI 1.1% to 8.8%) probability of being a C282Y homozygote. CONCLUSIONS: A large primary care-based sample of 44,809 participants contributed to the development of a new computer⁄smartphone tool that predicts the probability of being a C282Y homozygote of the HFE gene from serum ferritin and transferrin saturation values.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".