Does chronic hyperglycaemia increase the risk of kidney stone disease? results from a systematic review and meta-analysis
Bibliographic record
Abstract
DESIGN: Systematic review and meta-analysis of observational studies was performed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for studies reporting on diabetes mellitus (DM) or metabolic syndrome (MetS) and kidney stone disease (KSD). OBJECTIVE: To examine the association between chronic hyperglycaemia, in the form of DM and impaired glucose tolerance (IGT) in the context of MetS and KSD. SETTING: Population-based observational studies. Databases searched: Ovid MEDLINE without revisions (1996 to June 2018), Cochrane Library (2018), CINAHL (1990 to June 2018), ClinicalTrials.gov, Google Scholar and individual journals including the Journal of Urology, European Urology and Kidney International. PARTICIPANTS: Patients with and without chronic hyperglycaemic states (DM and MetS). MAIN OUTCOME MEASURES: English language articles from January 2001 to June 2018 reporting on observational studies. EXCLUSIONS: No comparator group or fewer than 100 patients. Unadjusted values were used for meta-analysis, with further meta-regression presented as adjusted values. Bias was assessed using Newcastle-Ottawa scale. RESULTS: 2340 articles were screened with 13 studies included for meta-analysis, 7 DM (three cohort) and 6 MetS. Five of the MetS studies provided data on IGT alone. These included: DM, n=28 329; MetS, n=31 767; IGT, n=12 770. CONTROLS: DM, n=5 89 791; MetS, n=1 78 050; IGT, n=2 93 852 patients. Adjusted risk for DM cohort studies, RR=1.23 (0.94 to 1.51) (p<0.001). Adjusted ORs for: DM cross-sectional/case-control studies, OR=1.32 (1.21 to 1.43) (p<0.001); IGT, OR=1.26 (0.92 to 1.58) (p<0.0001) and MetS, OR=1.35 (1.16 to 1.54) (p<0.0001). There was no significant difference between IGT and DM (cross-sectional/case-control), nor IGT and MetS. There was a moderate risk of publication bias. Statistical heterogeneity remained significant in adjusted DM cohort values and adjusted IGT (cross-sectional/case-control), but non-signficant for adjusted DM (cross-sectional/case-control). CONCLUSION: Chronic hyperglycaemia increases the risk of developing kidney stone disease. In the context of the diabetes pandemic, this will increase the burden of stone related morbidity and mortality. PROSPERO REGISTRATION NUMBER: CRD42018093382.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".