Bibliographic record
Abstract
Background: It is suggested that targeted screening for hemochromatosis and iron overload may be worthwhile. The aim of this study was to examine uric acid as a potential indicator of the presence of iron overload. Methods: We analyzed adults aged 20 and older in the National Health and Nutrition Examination Survey 1999 to 2002. We computed logistic regressions controlling for age, sex, race/ethnicity, liver or kidney condition, and alcohol use to see the relationship between combinations of uric acid and ferritin with the outcomes of elevated liver enzymes and proteinuria. Results: In unadjusted analyses, 20.7% of individuals with high uric acid had high ferritin levels versus 8.8% of individuals with low uric acid levels (P < .001). Individuals with both elevated uric acid and elevated ferritin levels had significantly higher liver enzymes than individuals with either elevated uric acid or ferritin. With low uric acid and low ferritin as the reference category, individuals with high uric acid and high ferritin were significantly more likely to also have proteinuria (odds ratio, 2.66; 95% CI, 1.82–3.91). Conclusions: Elevated levels of uric acid is associated with elevated ferritin levels and may serve as a risk stratification variable for presence of iron overload and hemochromatosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.035 | 0.024 |
| Insufficient payload (model declined to judge) | 0.041 | 0.022 |
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".