Prevalence of diabetes in people with intellectual disabilities and age‐ and gender‐matched controls: A meta‐analysis
Bibliographic record
Abstract
Abstract Background This meta‐analysis aims to: (i) describe the pooled prevalence of diabetes in people with intellectual disabilities, (ii) investigate the association with demographic, clinical and treatment‐related factors and (iii) compare the prevalence versus age‐ and gender‐matched general population controls. Methods Pubmed, Embase and CINAHL were searched until 01 May 2021. Random effects meta‐analysis and an odds ratio analysis were conducted to compare rates with controls. Results The trim‐ and fill‐adjusted pooled diabetes prevalence amongst 55,548 individuals with intellectual disabilities (N studies = 33) was 8.5% (95% CI = 7.2%–10.0%). The trim‐ and fill‐adjusted odds for diabetes was 2.46 times higher (95% CI = 1.89–3.21) (n = 42,684) versus controls (n = 4,177,550). Older age (R2 = .83, p < .001), smoking (R2 = .30, p = .009) and co‐morbid depression (R2 = .18, p = .04), anxiety (R2 = .97, p < .001), and hypertension (R2 = 0.29, p < .001) were associated with higher diabetes prevalence rates. Conclusions Our findings demonstrate that people with intellectual disabilities are at an increased risk of diabetes, and therefore routine screening and multidisciplinary management of diabetes is needed.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".