Anemia in kidney transplanted patients
Bibliographic record
Abstract
BACKGROUND: Although a known cardiovascular risk factor, anemia in the renal transplant recipients has only recently been receiving an increasing attention. METHODS: In a cross-sectional study, data was obtained from 959 patients followed at a single outpatient transplant clinic. Based on the guideline of the American Society of Transplantation, anemia was defined as hemoglobin (Hb) < or =130 g/L in males and < or =120 g/L in females. RESULTS: About one-third (34%) of the patients were anemic. The prevalence of anemia was comparable in males and females. Serum Hb concentration was significantly correlated with the estimated glomerular filtration rate (eGFR) (abbreviated modification of diet in renal disease formula) (r = 0.266, p < 0.001), serum transferrin (r = 0.268, p < 0.001) and serum albumin (r = 0.196, p < 0.001). None of the immunosuppressive medications or the use of angiotensin converting enzyme inhibitors was associated with a higher likelihood of anemia. In multivariate analysis the eGFR, serum albumin and serum transferrin, potential markers of nutritional status and/or chronic inflammation, and also iron deficiency were independently and significantly associated with anemia. Erythropoietin was administered only to 63 (19%) anemic patients. CONCLUSIONS: Post-transplant anemia is a prevalent and under-treated condition. Based on our results we suggest that, besides other factors, protein/energy malnutrition and/or chronic inflammation may be independently associated with anemia. Further studies are needed to determine whether the presence of anemia and its treatment will have an impact on long-term outcomes of this population.
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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 | 0.002 |
| 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 | 0.000 |
| 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".