Clinical and Laboratory Associations with Persistent Hyperferritinemia in 373 Black Hemochromatosis and Iron Overload Screening Study Participants
Bibliographic record
Abstract
BACKGROUND: 373 black participants had elevated screening and post-screening serum ferritin (SF) (> 300 μg/L men; > 200 μg/L women). MATERIAL AND METHODS: We retrospectively studied SF and post-screening age; sex; body mass index; transferrin saturation (TS); ALT; AST; GGT; elevated C-reactive protein; ß-thalassemia; neutrophils; lymphocytes; monocytes; platelets; metacarpophalangeal joint hypertrophy; hepatomegaly; splenomegaly; diabetes; HFE H63D positivity; iron/alcohol intakes; and blood/erythrocyte transfusion units. Liver disease was defined as elevated ALT or AST. We computed correlations of SF and TS with: age; body mass index; ALT; AST; GGT; C-reactive protein; blood cell counts; and iron/alcohol. We compared participants with SF > 1,000 and ≤ 1,000 μg/L and performed regressions on SF. RESULTS: There were 237 men (63.5%). Mean age was 55 ± 13 (SD) y. 143 participants had liver disease (62 hepatitis B or C). There were significant correlations of SF: TS, ALT, AST, GGT, and monocytes (positive); and SF and TS with platelets (negative). 22 participants with SF > 1,000 μg/L had significantly higher median TS, ALT, and AST, and prevalences of anemia and transfusion > 10 units; and lower median platelets. Regression on SF revealed significant associations: TS; male sex; age; GGT; transfusion units (positive); and splenomegaly (negative) (p < 0.0001, 0.0016, 0.0281, 0.0025, 0.0001, and 0.0096, respectively). Five men with SF > 1,000 μg/L and elevated TS had presumed primary iron overload (hemochromatosis). Four participants had transfusion iron overload. CONCLUSION: Persistent hyperferritinemia in 373 black adults was associated with male sex, age, TS, GGT, and transfusion. 2.4% had primary iron overload (hemochromatosis) or transfusion iron overload.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".