Renal function is independently associated with red cell distribution width in kidney transplant recipients: a potential new auxiliary parameter for the clinical evaluation of patients with chronic kidney disease
Bibliographic record
Abstract
Red cell distribution width (RDW), a measure of heterogeneity in the size of circulating erythrocytes, reportedly predicts mortality. Similarly to RDW, impaired renal function is also associated with inflammation and protein-energy wasting. This study assessed if renal function is associated with RDW independent of relevant confounders in stable kidney transplant recipients. We examined the association between RDW and estimated glomerular filtration rate (eGFR) in a cohort of 723 prevalent kidney transplanted recipients who were not receiving erythropoietin-stimulating agents. Associations were examined in regression models adjusted for age, sex, comorbidity, blood haemoglobin, iron indices, markers of nutritional status and inflammation, markers of bone and mineral metabolism and the use of immune suppressants. Lower eGFR was significantly associated with higher RDW (r = -0·382, P < 0·001). This association remained highly significant even after multivariate adjustments where 10 ml/min decrease in the eGFR was significantly associated with an increase of the RDW values (B10 ml/min decrease = 0·078; 95% confidence interval: 0·044-0·111). The results were consistent in subgroups of patients with different levels of haemoglobin, chronic kidney disease status and various markers of inflammation and iron status. Lower eGFR is associated with higher RDW, independent of comorbidity, iron deficiency, inflammation and nutritional status in kidney transplant recipients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".