Serum ferritin is a biomarker for liver mortality in the Hemochromatosis and Iron Overload Screening Study
Bibliographic record
Abstract
BACKGROUND: We identified no reports of long-term follow-up of participants in hemochromatosis screening programs. We evaluated causes of death and survival in non-C282Y homozygous Canadian participants in the primary care-based hemochromatosis and iron overload screening (HEIRS) study. MATERIAL AND METHODS: Initial screening (IS) included transferrin saturation (TS), serum ferritin (SF), HFE genotyping (C282Y, H63D), and health questionnaire responses. By definition, participants without C282Y or H63D had HFE wt/wt. We linked 20,306 Canadian participants to the Ontario Death Registry for dates and causes of death 9 y after IS. We computed Cox proportional hazards to identify factors with increased death risks and Kaplan-Meier curves to estimate survival of non-C282Y homozygous participants with SF ≤ 1,000 μg/L and > 1,000 μg/dL. RESULTS: There were 19,052 evaluable participants (IS mean age 49 y; 60% women; 93 C282Y homozygotes). There were 988 deaths. Significantly increased hazard ratios for all-cause mortality were positively associated with TS, SF, men, and C282Y homozygosity, and liver disease, diabetes, and heart failure reports. Non-C282Y homozygous participants with SF > 1,000 μg/L had lower survival than those with SF ≤ 1,000 μg/L (p < 0.0001). CONCLUSIONS: Nine years after initial screening, non-C282Y homozygous participants and SF > 1,000 μg/L was associated with decreased survival.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".