Time in Range: Unveiling the Correlation with Diabetic Retinopathy in Type 2 Diabetes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: Research has established an association between glycemic control and retinopathy progression; however, the use of continuous glucose monitoring (CGM) and diabetic retinopathy (DR) progression remains less explored. Our study aims to explore the link between time in range (TIR) and DR and its clinical implications. Materials and methods: Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guideline, we conducted a systematic review by searching databases such as PubMed, EBSCO, and ProQuest, supplemented by manual exploration. Studies reporting TIR or other CGM-derived metrics in association with DR were included. The quality of each study was evaluated using the Newcastle-Ottawa Scale (NOS). Review Manager 5.4 software, was used to performed a meta-analysis with random-effects model. Results: The meta-analysis of five studies indicated significant associations between CGM-derived metrics and diabetic retinopathy. TIR exhibited a mean difference of –6.44 (95% CI: –8.10, –4.78, p < 0.001), standard deviation (SD) showed a mean difference of 0.20 (95% CI: 0.16, 0.24, p < 0.001), mean amplitude of glycemic excursion (MAGE) displayed a mean difference of 0.45 (95% CI: 0.31, 0.58, p < 0.001), and coefficient of variation (CV) demonstrated a mean difference of –0.99 (95% CI: 0.43, 1.55, p = 0.0006). Stratification by TIR percentage (< 70% vs. ≥ 70%) revealed an odds ratio of 2.06 (95% CI: 0.85, 4.97, p = 0.11) for diabetic retinopathy risk, although statistically insignificant. Conclusions: Lower TIR is significantly associated with DR in T2D patients. Furthermore, higher SD, MAGE, and CV were linked to the presence of DR.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| 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) | 0.003 | 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".