Hemochromatosis and Iron-Overload Screening in a Racially Diverse Population
Bibliographic record
Abstract
BACKGROUND: Iron overload and hemochromatosis are common, treatable conditions. HFE genotypes, levels of serum ferritin, transferrin saturation values, and self-reported medical history were studied in a multiethnic primary care population. METHODS: Participants were recruited from primary care practices and blood-drawing laboratories. Blood samples were tested for transferrin saturation, serum ferritin, and C282Y and H63D mutations of the HFE gene. Before genetic screening, participants were asked whether they had a history of medical conditions related to iron overload. RESULTS: Of the 99,711 participants, 299 were homozygous for the C282Y mutation. The estimated prevalence of C282Y homozygotes was higher in non-Hispanic whites (0.44 percent) than in Native Americans (0.11 percent), Hispanics (0.027 percent), blacks (0.014 percent), Pacific Islanders (0.012 percent), or Asians (0.000039 percent). Among participants who were homozygous for the C282Y mutation but in whom iron overload had not been diagnosed (227 participants), serum ferritin levels were greater than 300 mug per liter in 78 of 89 men (88 percent) and greater than 200 microg per liter in 79 of 138 women (57 percent). Pacific Islanders and Asians had the highest geometric mean levels of serum ferritin and mean transferrin saturation despite having the lowest prevalence of C282Y homozygotes. There were 364 participants in whom iron overload had not been diagnosed (29 C282Y homozygotes) who had a serum ferritin level greater than 1000 microg per liter. Among men, C282Y homozygotes and compound heterozygotes were more likely to report a history of liver disease than were participants without HFE mutations. CONCLUSIONS: The C282Y mutation is most common in whites, and most C282Y homozygotes have elevations in serum ferritin levels and transferrin saturation. The C282Y mutation does not account for high mean serum ferritin levels and transferrin saturation values in nonwhites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".