Initial Screening Transferrin Saturation Values, Serum Ferritin Concentrations, and HFE Genotypes in Native Americans and Whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study.
Bibliographic record
Abstract
Abstract There are few reports of transferrin saturation (TfSat) and serum ferritin (SF) phenotypes and HFE C282Y and H63D genotypes in native Americans. We compared initial screening data of 645 native American and 43,453 white HEIRS Study participants who reported a single race/ethnicity and who did not report a previous diagnosis of hemochromatosis or iron overload. Each underwent TfSat and SF measurements without regard to fasting, and HFE C282Y and H63D genotyping. Elevated measurements were defined as: TfSat >50% (men), >45% (women); and SF >300 ng/mL (men), >200 ng/mL (women). Mean TfSat was lower in native American men than in white men (31% vs. 32%, respectively; p = 0.0337), and lower in native American women than in white women (25% vs. 27%, respectively; p <0.0001). Mean SF was similar in native American and white men (153 μg/L vs. 151 μg/L; p = 0.8256); mean SF was lower in native American women than in white women (55 μg/L vs. 63 μg/L, respectively; p = 0.0015). The respective percentages of native American men and women with elevated TfSat or elevated SF were similar to those of white men and women. The respective mean TfSat and SF values of native American and white participants with genotype HFE wt/wt were similar. The C282Y allele frequency was 0.0340 in native Americans and 0.0683 in whites (p <0.0001). The H63D allele frequency was 0.1150 in native Americans and 0.1532 in whites (p = 0.0001). We conclude that the screening TfSat and SF phenotypes of native Americans do not differ greatly from those of whites. The respective allele frequencies of HFE C282Y and H63D are significantly lower in native Americans than in whites.
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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 | 0.001 | 0.000 |
| 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".