Hyperferritinemia in the Chinese and Asian Community: A Retrospective Review of the University of British Columbia Experience
Bibliographic record
Abstract
BACKGROUND AND METHODS: Elevated serum ferritin is a common clinical finding. The etiology of hyperferritinemia in the Asia-Pacific population is less clear due to a low prevalence of known HFE mutations such as C282Y and H63D, as well as an increased prevalence of viral hepatitis and hereditary anemia. A retrospective case review of 80 patients of Asian ethnicity referred to three subspecialists in tertiary care teaching hospitals between January 1997 and March 2005 for assessment of hyperferritinemia was performed. RESULTS: Only four patients (5%) had iron overload on liver biopsy or quantitative phlebotomy. Forty-nine patients (61%) had secondary causes for their hyperferritinemia, of which 26 had liver disease; 16 of those patients also had viral hepatitis. Thirteen patients fulfilled criteria for the insulin resistance syndrome. Other causes included hematological disorders (n=10), malignancy (n=2) and inflammatory arthritis (n=2). Twenty-seven cases (34%) of unexplained hyperferritinemia were found. Of a total of 22 patients who underwent liver biopsy, significant iron deposition was found in one patient. Fifteen patients underwent C282Y and H63D genotyping, with two cases of H63D heterozygosity. Fourteen patients had first-degree relatives with hyperferritinemia. Three families were identified with more than two members affected, which is suggestive of a possible hereditary hyperferritinemia syndrome. CONCLUSION: Secondary causes of elevated ferritin in the Asian population, particularly liver disease, are common, but primary iron overload syndromes appear to be rare. In a significant proportion of patients, the etiology remains unexplained. The genetic basis for hyperferritinemia in Asians is poorly defined and requires further study.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".