PD12-04 CHRONIC HYPERGLYCAEMIA INCREASES THE RISK OF KIDNEY STONE DISEASE, RESULTS FROM A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
You have accessJournal of UrologyStone Disease: Epidemiology & Evaluation II (PD12)1 Apr 2019PD12-04 CHRONIC HYPERGLYCAEMIA INCREASES THE RISK OF KIDNEY STONE DISEASE, RESULTS FROM A SYSTEMATIC REVIEW AND META-ANALYSIS Robert Geraghty, Sarah Prattley*, Abdihakim Maalim, Bhaskar Somani, Paul Cook, and Paul Roderick Robert GeraghtyRobert Geraghty More articles by this author , Sarah Prattley*Sarah Prattley* More articles by this author , Abdihakim MaalimAbdihakim Maalim More articles by this author , Bhaskar SomaniBhaskar Somani More articles by this author , Paul CookPaul Cook More articles by this author , and Paul RoderickPaul Roderick More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555372.20468.63AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Examination of association between chronic hyperglycaemia, in the form of diabetes mellitus (DM) and impaired glucose tolerance (IGT) in the context of metabolic syndrome (MetS), and the risk of kidney stone disease (KSD) METHODS: P-Chronic hyperglycaemic (DM and IGT) C-Without hyperglycaemia O-KSD S-Systematic review and meta-analysis of published observational studies (cohort, case control and cross-sectional) using PRISMA guidelines for studies reporting on DM or MetS and KSD. English language articles from January 2001-June 2018 reporting on all observational studies. Studies were excluded if there was no comparator group or fewer than 100 patients. Both unadjusted and adjusted (where reported) values were identified and used for meta-analysis. Risk is presented as RR for cohorts and OR for case-control and cross-sectional studies. Bias was assessed using the Newcastle-Ottawa quality assessment scale. PROSPERO registration number CRD42018093382. RESULTS: 2,340 articles were screened with 13 studies included for meta-analysis, 7 DM (3 cohort, 3 cross-sectional*, 3 case-control) and 6 MetS (all cross-sectional), with 5 providing data on IGT alone. These included 28,329 patients with DM, 31,767 patients with MetS and 12,770 with IGT. Controls included; DM: 589,791 patients, MetS: 178,050 patients and IGT: 293,852 patients. Adjusted risk for DM cohort studies was RR=1.23 (0.94-1.51) (p<0.001) (see fig. 1), for DM cross-sectional/case-control studies were OR=1.32 (1.21-1.43) (p<0.001), for IGT cross-sectional studies was OR=1.26 (0.92-1.58) (p<0.001) (see fig. 2) and MetS cross-sectional studies was OR=1.35 (1.16-1.54) (p<0.001). Risk of bias was moderate. CONCLUSIONS: Chronic hyperglycaemia increases the risk of developing kidney stone disease. In the context of the diabetes pandemic, this increases the risk of stone related morbidity and mortality. Source of Funding: None Southampton, United Kingdom© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e221-e222 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Robert Geraghty More articles by this author Sarah Prattley* More articles by this author Abdihakim Maalim More articles by this author Bhaskar Somani More articles by this author Paul Cook More articles by this author Paul Roderick More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".