Hepatic and cardiac iron overload quantified by magnetic resonance imaging in patients on hemodialysis: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Few studies have reported hepatic and cardiac iron overload in patients with end-stage renal disease (ESRD), and the current evidence regarding the prevalence is still scarce. AIM: This review aims to estimate the prevalence of hepatic and/or cardiac iron overload quantified by magnetic resonance imaging (MRI) in patients with ESRD who receive hemodialysis (HD), peritoneal dialysis (PD), or have undergone a kidney transplant. METHODS: A systematic review with meta-analysis was conducted and reported in line with PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. MEDLINE and Embase bibliographic databases were searched using a comprehensive list of controlled vocabulary and keywords to identify relevant studies. All studies reporting the prevalence of hepatic and/or cardiac iron overload quantified by MRI in ESRD patients were considered. The Newcastle-Ottawa scale was used to assess the methodological quality of included studies. To investigate the heterogeneity between studies, random-effect meta-analyses for proportions were used. RESULTS: The review comprised seven studies that included 339 patients. Using meta-analysis, the pooled prevalence of severe and mild to moderate hepatic iron overload quantified by MRI was 0.23 [95% CI: 0.08-0.43] and 0.52 [95% CI: 0.47-0.57], respectively. Only three studies included cardiac iron quantification, and none reported iron overload. CONCLUSIONS: This review has revealed a high prevalence of severe hepatic iron overload in patients with ESRD treated by HD. Further studies with a larger sample size are needed to determine the impact of iron overload on vital organs in patients with ESRD and guide future research in this understudied field. Proper use of iron chelation and continuous monitoring will help in the early detection of unsolicited complications; however, the low renal clearance of most iron chelators limits the options for treating iron excess in patients with ESRD.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".