Anaemia, cardiovascular disease and kidney disease: integrating new knowledge in 2002
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review assesses recent publications, from 2001 until the present, which address the relationship between cardiovascular disease (CVD) and anaemia in patients with chronic kidney disease. RECENT FINDINGS: Insights from the recently published basic science literature have helped to place findings from clinical studies into a new context, and thereby assist us to understand and further explore the complex relationship between haemoglobin level and survival in chronic kidney disease. The effects of erythropoietin molecules and the presence of receptors in vascular endothelium, myocardium and other tissues are described. Both observational and interventional clinical studies are examined, and limitations in the methodology and statistical analysis of clinical studies are emphasized, but are given context within the body of literature preceding the past year's publications. SUMMARY: Data suggest that development of CVD in patients with kidney disease is multifactorial. Several factors associated with CVD are also associated with anaemia, thereby making causal arguments for the role of anaemia in CVD and survival difficult. Arguments are made for the importance of prevention of anaemia and of individualizing therapeutic goals for its treatment. Well designed prospective studies with both CVD events and mortality as outcomes, and with enrollment beginning before the start of dialysis, are essential if we are to determine the optimal therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".