Effect of erythropoietin levels on mortality in old age: the Leiden 85-plus Study
Bibliographic record
Abstract
BACKGROUND: The production of erythropoietin is triggered by impaired oxygen delivery to the kidney, either because of anemia or hypoxemia. High erythropoietin levels have been shown to predict the risk of death among patients with chronic heart failure. We investigated the prognostic value of elevated erythropoietin levels on mortality among very elderly people in the general population. METHODS: The Leiden 85-plus Study is a population-based prospective follow-up study involving 599 people aged 85 years in Leiden, the Netherlands, enrolled between September 1997 and September 1999. Erythropoietin levels were determined at age 86. For this analysis, we included 428 participants with a creatinine clearance of at least 30 mL/min. Mortality data, recorded until Feb. 1, 2008, were obtained from the municipal registry. RESULTS: During follow-up, 324 (75.7%) participants died. Compared with participants whose erythropoietin levels were in the lowest tertile (reference group), those whose levels were in the middle tertile had a 25% increased risk of death (hazard ratio [HR] 1.25, 95% confidence interval [CI] 0.95-1.64), and those whose levels were in the highest tertile had a 73% increased risk (HR 1.73, 95% CI 1.32-2.26) (p value for trend < 0.01). The association between erythropoietin levels and mortality remained largely unchanged after we adjusted for sex, creatinine clearance, hemoglobin level, comorbidity, smoking status and C-reactive protein level, and was similar for deaths from cardiovascular and noncardiovascular causes. INTERPRETATION: Among people aged 85 years and older, elevated erythropoietin levels were associated with an increased risk of death, independent of hemoglobin levels.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".