Anemia Is Associated with Mortality in Kidney-Transplanted Patients—A Prospective Cohort Study
Bibliographic record
Abstract
Although anemia is a known risk factor of mortality in several patient populations, no prospective study to date has demonstrated association between anemia and mortality in kidney-transplanted patients. In our prospective cohort study (TransQol-HU Study), we tested the hypothesis that anemia is associated with mortality and graft failure (return to dialysis) in transplanted patients. Data from 938 transplanted patients, followed at a single outpatient transplant center, were analyzed. Sociodemographic parameters, laboratory data, medical history and information on comorbidity were collected at baseline. Data on 4-year outcome (graft failure, mortality or combination of both) were collected prospectively from the patients' charts. Both mortality and graft failure rate during the 4-year follow-up was significantly higher in patients who were anemic at baseline (for anemic vs nonanemic patients, respectively: mortality 18% vs. 10%; p < 0.001; graft failure 17% vs 6%; p < 0.001). In multivariate Cox proportional hazard models the presence of anemia significantly predicted mortality (HR = 1.690; 95% CI: 1.115-2.560) and also graft failure (HR = 2.465; 95% CI: 1.485-4.090) after adjustment for several covariables. Anemia, which is a treatable complication, is significantly and independently associated with mortality and graft failure in kidney-transplanted patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".