Diagnosing and preventing iron overload
Bibliographic record
Abstract
Absolute or functional iron (Fe) deficiency is an important determinant of anemia in hemodialysis patients and parenteral Fe is routinely used to treat this condition in conjunction with erythropoiesis stimulating agents. While restoration of hemoglobin toward the target range is a good outcome of Fe replacement, it is well known that Fe overload and toxicity may be adverse consequences of this therapy. Dialysis clinical practice guidelines recommend tailoring Fe therapy based on transferrin saturation and serum ferritin levels. Unfortunately, serum Fe markers may not accurately reflect the amount of Fe in the body, because factors such as infections, inflammation, or malignancy can alter serum ferritin levels. Some recent trials in dialysis patients receiving high intravenous Fe doses have shown increased cardiovascular morbidity and mortality and studies using magnetic resonance imaging (MRI) in this population have shown excessive tissue liver iron content (LIC) suggesting Fe overload. While LIC measured by MRI correlates well with LIC quantitated by liver biopsy, it only represents a surrogate marker for total body Fe and its clinical relevance in dialysis patients in terms of mortality and morbidity remains to be demonstrated. Nevertheless, these recent findings challenge the use of current serum Fe markers recommended by clinical guidelines to guide safe Fe therapy in dialysis patients. While not yet established for the routine screening of dialysis patients for Fe overload, MRI should be considered in patients who have received a high cumulative dose of intravenous Fe, or have long cumulative dialysis vintage. Further studies are needed to assess how MRI will alter management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".