The Diversity of Liver Diseases among Outpatient Referrals for Elevated Serum Ferritin
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to examine the diversity of liver diseases in outpatients referred because of elevated serum ferritin. METHODS: A retrospective review was performed of outpatient referrals for serum ferritin elevations made to a tertiary care centre liver clinic between 1999 and 2005. Information regarding serum ferritin, transferrin saturation, liver biopsy, liver iron concentration and final diagnosis was extracted. Patients were stratified into two groups based on ferritin concentration: ferritin concentration 300 microg/L to 1000 microg/L, and ferritin concentration greater than 1000 microg/L. RESULTS: A total of 482 charts were reviewed, of which 119 (25%) had ferritin concentrations greater than 1000 microg/L. HFE-linked hemochromatosis, nonalcoholic steatohepatitis and alcohol-related liver disease were the top three diagnoses. HFE-linked hemochromatosis accounted for 28% to 42% of the diagnoses in all subgroups. The percentage of patients diagnosed with HFE-linked hemochromatosis was similar in the ferritin 300 microg/L to 1000 microg/L and the ferritin greater than 1000 microg/L groups (P = 0.067). Among patients with ferritin greater than 1000 microg/L, 63% underwent a liver biopsy. Of those with an elevated liver iron concentration (greater than 35 micromol/g dry weight), 71% had a transferrin saturation greater than 50% (88% of C282Y homozygotes and 43% of non-C282Y homozygotes). In non-C282Y homozygotes with an elevated serum ferritin concentration greater than 1000 microg/L, 64% did not have iron overload on liver biopsy. CONCLUSION: HFE-linked hemochromatosis accounted for less than one-half of the diagnoses in an outpatient population referred for elevated ferritin, suggesting a need to search further for an alternate cause.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".