Effect of anaemia on mortality, cardiovascular hospitalizations and end‐stage renal disease among patients with chronic kidney disease
Bibliographic record
Abstract
OBJECTIVE: To determine whether an independent association exists between anaemia and chronic kidney disease (CKD) outcomes in a quasi-incidence cohort when patients' most recent laboratory values are considered. METHODS: We conducted a dynamic, retrospective cohort study among patients with incident CKD in a large health maintenance organization administrative data set. CKD was defined by two estimated glomerular filtration rates (eGFR). We measured the absolute rates for all-cause mortality, cardiovascular hospitalizations and end-stage renal disease. RESULTS: Our completed cases Cox regression model followed 5885 patients with both CKD and haemoglobin measures. For patients with the most severe anaemia (haemoglobin <10.5 g/dL), we estimated an increased rate of mortality (hazard ratio (HR)=5.27, CI 4.37-6.35), cardiovascular hospitalizations (HR=2.18, CI 1.76-2.70) and end-stage renal disease (HR=5.46, CI 3.38-8.82) when compared with patients who were not anaemic; the HR reflect time-varying haemoglobins and eGFR. CONCLUSION: Anaemia is a predictor of excess mortality, excess cardiovascular hospitalizations and excess end-stage renal disease even when the progression of CKD is considered by controlling for time-varying eGFR values.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".