Predictors of anemia in patients on hemodialysis
Bibliographic record
Abstract
Even though the use of erythropoietin and intravenous iron has improved the treatment of anemia in hemodialysis patients, a considerable proportion of these patients still have anemia. The aim of this study was to identify predictors of anemia in a hemodialysis population. In a single-center hemodialysis unit, all patients were studied with blood tests and their medication recorded during a period of 22 months. Correlations with hemoglobin (Hb) were performed with a simple regression or a t test. Variables that reached 5% significance were entered in a multiple regression analysis. Selected variables were presented in quartiles with levels of Hb. Mean Hb was 11.3 g/dL, and 53 patients (40%) had Hb<11.0 g/dL. In the simple regression analysis Hb correlated positively with s-iron, CHr, s-albumin, and doses of sevelamer, and negatively with sedimentation rate (SR), ferritin, base excess, and doses of erythropoietin. In the multiple regression analysis erythrocytes SR was the only variable that remained significant. Elevated SR is the strongest predictor of anemia in hemodialysis patients receiving adequate treatment with erythropoietin and intravenous iron. Patients using high doses of sevelamer had higher Hb levels than patients using low doses.
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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 | 0.000 | 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".