Heparin and related substances for preventing diabetic kidney disease
Bibliographic record
Abstract
BACKGROUND: Diabetic kidney disease (DKD, also called diabetic nephropathy, DN) is the major cause of end-stage kidney disease (ESKD) in many countries and is associated with increased morbidity and mortality as compared to other causes of kidney disease. One of the pathological changes of DKD is the thickening of the glomerular basement membrane, mesangial expansion and proliferation. The presence of the glycosaminoglycan side chains of heparan sulfate proteoglycan, an important constituent of the glomerular basement membrane, is decreased in DKD proportionally to the increasing degree of proteinuria. Research on animals has suggested that heparin and related substances may prevent glomerular membrane thickening. However, it is not known whether heparin and related substances can prevent the onset of DKD and, therefore, be recommended for primary prevention of this condition. OBJECTIVES: To assess the benefits and harms of heparin and related substances for preventing the onset of DKD. SEARCH STRATEGY: We searched the Cochrane Renal Group's Specialised Register and the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library (Issue 2, 2009). We also searched MEDLINE (1966 to June 2009), EMBASE (1980 to June 2009), China Biological Medicine (CBM; 1979 to June 2009), VIP Chinese Science and Technique Journals Database (until June 2009), China National Infrastructure (CNKI) (until June 2009) and Wanfang database (until June 2009). Reference lists of nephrology textbooks, review articles and relevant studies were also searched. SELECTION CRITERIA: All relevant randomised controlled trials (RCTs) and quasi-RCTs looking at the benefits and harms of heparin and related substances for preventing the onset of DKD were eligible. DATA COLLECTION AND ANALYSIS: We planned for two authors to extract data independently using a self-developed data extraction form and enter them into RevMan 5 software; for meta-analyses to be performed when more than one study provided data on a comparable outcome on sufficiently similar patients; for random-effects analyses to be performed whenever heterogeneity between results appeared to be present; and for standardised differences in mean outcome measures to be used due to the use of different scales and periods of treatment. MAIN RESULTS: No studies met our inclusion criteria. AUTHORS' CONCLUSIONS: Rigorously well-designed, randomised, multi-centre, large-sample studies of heparin and related substances for preventing the onset of DKD are needed.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".