Vitamin D Insufficiency and Liver Iron Concentration in Transfusion Dependent Hemoglobinopathies in British Columbia
Bibliographic record
Abstract
Patients with thalassemia major (TM) and other chronically transfused hereditary anemias are at increased risk of complications including endocrinopathies and bone disease due to iron overload. Vitamin D is important for bone health. Vitamin D deficiency is common in patients with transfusional iron overload, and the mechanism remains unclear. The first step in vitamin D metabolism, hydroxylation, occurs in the liver and liver iron overload may interfere with this step. This study investigates an association between degree of liver iron overload and vitamin D levels in patients with transfusion dependent hemoglobinopathies. Patients with TM, hemoglobin Eβ TM (Eβ TM), and congenital dyserythropiotc anemia (CDA) attending the Inherited Bleeding and Red Blood Cell Disorder Program in British Columbia (IBRBCD BC), Canada were identified. Included patients had an assessment of liver iron concentration (LIC) by MRI and endocrinology assessment including 25 hydroxy vitamin D level. Thirty patients were identified. The mean LIC was 5.13 mg/g dry weight (DW). Vitamin D deficiency/insufficiency was identified in 19 (63.3%). Eleven (36.7%) patients had an LIC ≥5 mg/gDW, 8 of whom had a vitamin D level 5 mg/gDW and vitamin D level <60nmol/L (P= 0.027) and there was a significant inverse correlation between LIC and vitamin D (R=-0.33). These results indicate an association between increased LIC and vitamin D insufficiency/deficiency, suggesting that liver iron overload may indeed affect vitamin D metabolism. Prospective trials are needed to confirm these results.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".