Iron storage indices and risk of bacterial infections in hemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Infection is the second leading cause of death among hemodialysis (HD) patients. Because iron overload may be a risk factor for bacterial infection, concerns about excessive use of intravenous (IV) iron have arisen. In this retrospective analysis, we explored the relationship between target iron storage indices, as outlined in the Dialysis Outcomes Quality Initiative (DOQI) guidelines, and the incidence of bacterial infections. METHODS: We reviewed the charts of 87 HD patients who received their first course of IV iron at our dialysis unit between 1997 and 2001. Transferrin saturation (TSAT) rate, ferritin level, and other clinical/laboratory measures were recorded at baseline. Patients were followed for up to 2 years for the outcomes of bacteremia and bacterial pneumonia and censored at death, end-of-study observation, or kidney transplantation. Cox proportional hazards regression was used to evaluate the relationship of bacterial infections to iron storage indices. RESULTS: Thirty-two patients had at least one episode of bacterial infections. In multivariate analyses, after adjusting for sex and venous catheter use, iron-replete state (ferritin > 100 ng/mL and TSAT > 20%) was associated with a threefold higher risk of bacterial infections (95% CI 1.3-6.6; p = 0.01). Although diabetes mellitus and lower serum albumin had a nonsignificant trend toward an increased risk of bacterial infections, no such relationship was seen with the first 3-month cumulative IV iron dose. CONCLUSIONS: This study suggests an increased risk for bacterial infections at modest levels of iron stores (ferritin > 100 ng/mL and TSAT > 20%) among HD patients initiating IV iron. Large prospective studies are needed to confirm these relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".