Bibliographic record
Abstract
Objectives: Diabetes is very common, particularly in minority ethnic groups and the disadvantaged and is associated with multimorbidity, disability and premature death.In 2022 alone, diabetes was associated with 7000 excess deaths in the United Kingdom.Diabetes self-management education (DSME) is a cornerstone of therapy and has been shown to improve self-management skills, wellbeing and blood sugar balance; but does DSME prevent premature death?The aim of this scoping review was to examine the impact of DSME on mortality in people with type 2 diabetes.Methods: Using CINAHL, MEDLINE and EMBASE, two independent reviewers examined full-text articles reporting impact of DSME on mortality.Using a mixedmethods appraisal, the log odds ratio (logOR) with 95% confidence interval (CI) of mortality in those with and without DSME attendance was calculated and presented as Forest plots of the effect estimates of the included studies.It is noteworthy that the majority of studies report mortality as drop out rather than as a primary outcome measure.Results: From 294 articles, 32 studies with 18,567 participants were included in the analysis.Overall pooled mortality was 0.4% (n = 742).DSME attendance was associated with a significantly lower mortality rate (2.5% vs. 5.5%, p < 0.05).Compared with controls, logOR for DSME attendance was 0.31 (95% CI: 0.038-0.578).The accompanying funnel plot was symmetrical, indicating a low risk of publication bias. Conclusions:In this scoping review in people with type 2 diabetes, compared with controls, DSME attendance was associated with a significantly lower mortality risk.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.766 | 0.559 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".