Supplementary Material for: The impact of SGLT2 inhibitors on dementia onset in patients with type 2 diabetes - A meta-analysis of cohort studies
Bibliographic record
Abstract
Introduction: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have demonstrated neuroprotective effects and hold potential advantages in enhancing cognitive function. This study aims to clarify the association between SGLT2 inhibitors and the risk of dementia among individuals diagnosed with type 2 diabetes (T2D). Methods: All cohort studies concerning the impact of SGLT2 inhibitors on dementia onset in patients with T2D were identified. The literature search encompassed PubMed, Embase, Cochrane Library, and Web of Science from establishment to Mar 2024, with no language restriction. The quality of the literature was evaluated using the Newcastle-Ottawa Scale (NOS). Meta-analysis was conducted using RevMan5.4 software, calculating pooled risk ratios (RRs) with 95% confidence intervals (CI) for dichotomous outcomes. Results: Five cohort studies encompassing a total of 331908 patients were included in the analysis. The findings showed that individuals receiving SGLT2 inhibitors had a lower risk of dementia (I2 = 42%, P = 0.14; RR: 0.77; 95% CI: 0.71-0.84) compared to the control group. Subgroup analyses confirmed the consistent beneficial effects of SGLT2 inhibitors across different study regions (I2=0%, P=0.60) and genders (I2=0%, P=0.50). Conclusions: SGLT2 inhibitors may reduce the dementia risk in T2D patients. Given the limitations of the study, further investigations were warranted to confirm the benefits.
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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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.569 | 0.024 |
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".