Screening for Iron Overload: Lessons from the HEmochromatosis and IRon Overload Screening (HEIRS) Study
Bibliographic record
Abstract
BACKGROUND: The HEmochromatosis and IRon Overload Screening (HEIRS) Study provided data on a racially, ethnically and geographically diverse cohort of participants in North America screened from primary care populations. METHODS: A total of 101,168 participants were screened by testing for HFE C282Y and H63D mutations, and measuring serum ferritin concentration and transferrin saturation. In the present review, lessons from the HEIRS Study are highlighted in the context of the principles of screening for a medical disease as previously outlined by the World Health Organization. RESULTS: Genetic testing is well accepted, with minimal risk of discrimination. Transferrin saturation has high biological variability and relatively low sensitivity to detect HFE C282Y homozygotes, which limits its role as a screening test. Symptoms attributable to HFE C282Y homozygosity are no more common in individuals identified by population screening than in control subjects. CONCLUSIONS: Generalized population screening in a primary care population as performed in the HEIRS Study is not recommended. There may be a role for focused screening in Caucasian men, with some debate regarding genotyping followed by phenotyping, or phenotyping followed by genotyping.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".