Population screening for hemochromatosis: A comparison of unbound iron-binding capacity, transferrin saturation, and C282Y genotyping in 5,211 voluntary blood donors
Bibliographic record
Abstract
Early diagnosis and treatment of hemochromatosis is essential to prevent organ damage. Screening strategies to detect early hemochromatosis include testing for iron overload and/or genetic testing. Voluntary blood donors numbering 5,211 were screened with unbound iron-binding capacity (UIBC), transferrin saturation (TS), and genetic testing for the C282Y mutation of the HFE gene. The study found 16 C282Y homozygotes (1 in 327), 69 compound heterozygotes, 371 simple heterozygotes, and 4,755 normals. There were 5 men and 11 women homozygotes with a mean age of 42, range 28 to 57. Mean UIBC (24 +/- 7 microL) and TS (48% +/- 17%) in homozygotes were significantly different from compound heterozygotes, simple heterozygotes, and normals (ANOVA). Only 3 homozygotes had an elevated serum ferritin. Family studies found an additional 4 iron-loaded homozygotes. Optimal thresholds were < or =28 micromol/L for UIBC and > or =46% for TS. Receiver operating characteristic (ROC) curve analysis showed an area under the curve for UIBC of 0.93 (0. 85-1.0, 95% confidence interval), and for TS of 0.83 (0.7-0.95). Screening with UIBC to preselect those for genotyping is a cost-efficient strategy for population screening for hemochromatosis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".