Genetic screening for iron overload: No evidence of discrimination at 1 year.
Bibliographic record
Abstract
PURPOSE: This study measured the extent of insurance and employment problems associated with population screening for hereditary hemochromatosis and iron overload. METHODS: 101,168 primary care patients from the US and Canada were screened for iron phenotypes and HFE genotypes associated with hemochromatosis. Those identified to be at risk (2253) were offered a clinical examination, which 1677 (74%) accepted, and the 1154 of these who responded to an initial questionnaire about psychosocial issues were surveyed 1 year later about whether they had experienced problems with insurance or employment that they attributed to hereditary hemochromatosis and iron overload. RESULTS: 832 (72.1%) of the 1154 participants surveyed after 1 year responded to the second survey. Three (0.4%) had verified problems with insurance or employment that they believed were related to hereditary hemochromatosis and iron overload. Two had problems with life insurance, and one with long-term care insurance. All 3 had elevated iron levels but not a relevant HFE genotype. One of the life insurance problems was resolved; the second one was not serious. The participant who was denied long-term care insurance had other health conditions unrelated to hereditary hemochromatosis and iron overload that could have contributed to the denial. No problems were verified for health insurance or employment, or from any of the comparison group participants (controls and those with inconclusive screening results). CONCLUSIONS: The risk of insurance or employment problems 1 year after phenotype and genotype screening for hereditary hemochromatosis and iron overload is very low.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".