Iron indices and survival in maintenance hemodialysis patients with and without polycystic kidney disease
Bibliographic record
Abstract
BACKGROUND: Anemia is less prominent in patients with polycystic kidney disease (PKD). Such iron indices as ferritin and transferrin saturation (TSAT) values are used to guide management of anemia in individuals on maintenance hemodialysis (MHD). Optimal levels of correction of anemia and optimal levels of TSAT and ferritin are unclear in chronic kidney disease patients and have not been studied specifically in PKD. METHODS: We studied 2969 MHD patients with and 128 054 patients without PKD from 580 outpatient hemodialysis facilities between July 2001 and June 2006. Using baseline, time-dependent and time-averaged values with unadjusted and multivariable adjusted analysis models, the survival predictabilities of TSAT and ferritin were studied. RESULTS: PKD patients were 58 ± 13 years old and included 46% women, whereas non-PKD patients were 62 ± 15 years old and 45% women. In both PKD and non-PKD patients, a time-averaged TSAT between 30 and 40% was associated with the lowest mortality. Time-averaged ferritin between 100 and <800 ng/mL was associated with the lowest mortality in PKD patients, although this range was 500 to <800 ng/mL in non-PKD patients. CONCLUSIONS: In MHD patients with and without PKD, there was a U-shaped relationship between the average TSAT and mortality, and a TSAT of 30-40% was associated with the best survival. However, an average ferritin of 100-800 ng/mL was associated with the best survival in PKD patients, whereas that of non-PKD patients was 500-800 ng/mL. Further studies in PKD and non-PKD patients are necessary to determine whether or not therapeutic attempts to keep TSAT and ferritin levels in these ranges will improve survival.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".