ReviewDiagnostics (Basel, Switzerland)2023
Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Evaluation of Diabetes Risk Models Applied to the Prediction of Incident Dysglycaemia in African and European Populations.Diabetes, obesity & metabolism · 2026Article
- Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Developing a Simple Non-Laboratory-Based Machine Learning Tool for Prediabetes Screening in a Target Population: A Proof-of-Concept Study.Journal of diabetes science and technology · 2025Article
- External validation of the Hong Kong Chinese non-laboratory risk models and scoring algorithm for case finding of prediabetes and diabetes mellitus in primary care.Journal of diabetes investigation · 2024Article
- Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies' quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68-0.82), sensitivity (0.60-0.89), and specificity (0.50-0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31-0.79) and sensitivity (0.31-0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.