Cause of Death in Patients with Reduced Kidney Function
Bibliographic record
Abstract
Information on common causes of death in people with CKD is limited. We hypothesized that, as eGFR declines, cardiovascular mortality and mortality from infection account for increasing proportions of deaths. We calculated eGFR using the CKD Epidemiology Collaboration equation for residents of Alberta, Canada who died between 2002 and 2009. We used multinomial logistic regression to estimate unadjusted and age- and sex-adjusted differences in the proportions of deaths from each cause according to the severity of CKD. Cause of death was classified as cardiovascular, infection, cancer, other, or not reported using International Classification of Diseases codes. Among 81,064 deaths, the most common cause was cancer (31.9%) followed by cardiovascular disease (30.2%). The most common cause of death for those with eGFR≥60 ml/min per 1.73 m(2) and no proteinuria was cancer (38.1%); the most common cause of death for those with eGFR<60 ml/min per 1.73 m(2) was cardiovascular disease. The unadjusted proportion of patients who died from cardiovascular disease increased as eGFR decreased (20.7%, 36.8%, 41.2%, and 43.7% of patients with eGFR≥60 [with proteinuria], 45-59.9, 30-44.9, and 15-29.9 ml/min per 1.73 m(2), respectively). The proportions of deaths from heart failure and valvular disease specifically increased with declining eGFR along with the proportions of deaths from infectious and other causes, whereas the proportion of deaths from cancer decreased. In conclusion, we found an inverse association between eGFR and specific causes of death, including specific types of cardiovascular disease, infection, and other causes, in this cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".