Is There A Threshold of Hepatic Iron Concentration That Leads To Cirrhosis in C282y Hemochromatosis?
Bibliographic record
Abstract
OBJECTIVE: The relationship between the degree of iron overload and the presence of cirrhosis has not been clearly established in hemochromatosis. Severe iron overload occurs without cirrhosis and moderate iron overload can occur with cirrhosis. Previous estimates may have overestimated the problem because of the overdiagnosis of hemochromatosis in patients with alcoholic liver disease and chronic viral hepatitis. The objective of this study was to determine if a threshold for hepatic iron concentration leads to the development of cirrhosis in C282Y hemochromatosis. METHODS: This study included only hemochromatosis patients who were homozygotes for the C282Y mutation of the HFE gene and had undergone liver biopsy with hepatic iron concentration. Analysis of the thresholds for cirrhosis were determined using receiver operating characteristic (ROC) curve analysis. RESULTS: Data were available on 100 C282Y homozygotes (62 men, 38 women; mean age 51, range 18-74 yr). ROC curve analysis showed an area under the curve for hepatic iron concentration versus cirrhosis of 0.85 (95% CI = 0.75-0.96). The threshold for the prediction of cirrhosis was 283 micromol/g. At that threshold, the sensitivity was 85% and specificity 84%. CONCLUSIONS: From this analysis, it appears that a hepatic iron concentration >283 micromol/g is associated with cirrhosis. However, the low sensitivity of this threshold suggests that other cofactors contribute to the development of cirrhosis in hemochromatosis. Early diagnosis is encouraged to initiate iron depletion before the development of cirrhosis.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".