The Association Between Glycaemic Variability and Progression of Chronic Kidney Disease: a Systematic Review
Bibliographic record
Abstract
Abstract The study aims to evaluate the association between glycaemic variability and the risk of chronic kidney disease (CKD) progression in patients with diabetes and comorbid kidney disease. A comprehensive search was conducted of three databases from their inception to March 2022: Medline, Embase, and CINHAL. Publications were screened for eligibility and the quality of studies included was appraised using the Newcastle–Ottawa Scale. Extracted data were tabulated and reported in a narrative synthesis. Fourteen studies were included in the review providing data on 62,498 participants. Eight studies reported that greater glycaemic variability was associated with an increased incidence of CKD. Three studies reported an increased likelihood of CKD progression in those with high glycaemic variability, although the rate and risk of progression varied across the studies. Three studies reported an increased risk of progression to end-stage kidney disease (ESKD) with higher glycaemic variability. One study found that high glycaemic variability was associated with a decreased risk of progression to ESKD. Greater glycaemic variability was associated with the onset and progression of CKD. More research is required to verify whether glycaemic variability increases the risk of progression to ESKD in patients with diabetes and mild/moderate comorbid CKD.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".