Bibliographic record
Abstract
Several strategies are available to delay progression of renal disease and the development of associated co-morbidities. Hypertension is a strong independent risk factor for end-stage renal disease (ESRD) and there is consensus that blood pressure (BP) management is an important aspect of care in patients with chronic renal insufficiency (CRI). Clinical studies have shown that angiotensin-converting enzyme (ACE) inhibitors have renoprotective properties, independent of their antihypertensive effects, which can delay the onset of ESRD. Studies have also shown that intensive therapy of both type 1 and type 2 diabetes patients, to give near normal blood glucose concentrations, can reduce the incidence of progressive clinical proteinuria and may, therefore, protect against ESRD. Additionally, data are emerging that treatment of renal anaemia with epoetin can reduce mortality and delay the onset of dialysis in CRI patients, but these encouraging results need to be confirmed in large prospective studies. In conclusion, control of BP and hyperglycaemia, as well as use of ACE inhibitors and anaemia treatment, all have potential in delaying the progression of CRI or improving patient outcomes. If benefit is proven in future studies, these strategies will be most effective if implemented early in the course of CRI.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".