Cardiovascular disease and the kidney
Bibliographic record
Abstract
The interlinking of CVD with CKD is undeniable. CVD accounts for more than 50% of all morbidity and mortality in patients with kidney disease who have undergone renal replacement therapy, and CVD is also prevalent in patients with mild and moderately severe kidney disease. To help address the elevated risks of these patients, primary care physicians need to maintain vigilance in (1) identifying patients who have CKD and (2) implementing strategies for reducing the prevalence of CVD in this population. It is essential that patients be screened for relatively mild kidney disease by measurement of serum creatinine and urine microalbumin and by calculation of the glomerular filtration rate in mL/min/1.73 m2 using equations based on serum creatinine. Rigorous assessment of conventional risk factors, including dyslipidemia, hypertension, and diabetes, is also necessary to prevent the poor outcomes currently observed in persons with CKD. Routine use of ACE inhibitors and aspirin is encouraged in all patients with CKD, and strict glycemic and blood pressure control is recommended for optimal outcomes. In addition, patients should be screened and treated for risk factors particularly associated with kidney disease and CVD morbidity and mortality, including anemia, hyperphosphatemia, and hyperparathyroidism. Finally, physicians should be careful to avoid therapeutic nihilism in patients with kidney disease; those at highest risk of CVD are likely to receive the greatest benefit from cardiovascular therapies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".