Bibliographic record
Abstract
Cardiovascular morbidity and mortality are elevated in renally impaired patients, especially if they are hypertensive. Diabetes is also associated with a high prevalence of cardiovascular morbidity and end-stage renal disease. Albuminuria, elevated serum creatinine, decreased creatinine clearance and proteinuria independently predict cardiovascular risk. Even patients with mild renal impairment should be treated to slow kidney disease progression and reduce vascular damage. Blood pressure control is effective in reducing vascular complications of diabetes, but not all classes of antihypertensive agents provide renoprotection. Angiotensin-converting enzyme inhibitors are superior to beta-blockers in preventing or delaying the loss of kidney function associated with hypertension. The renoprotection appears to be in part independent of the antihypertensive effect. Angiotensin II receptor blockers (ARBs) also reduce the risk of renal complications in diabetics. Telmisartan seems well suited to provide renoprotection because, unlike other ARBs, it is almost exclusively excreted by the liver and no initial dose adjustment is required for patients with mild-to-moderate renal impairment. Other advantages of telmisartan include its very high volume of distribution and long terminal elemination half-life. Clinical trials to evaluate telmisartan will address the problems of diabetes, renal impairment and end-organ disease.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".