Hospital admissions among people with diabetes: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To describe the reasons for hospital admission among people with diabetes. METHODS: We searched Emcare, Embase, Medline and Google Scholar databases for population-based studies describing the causes of hospitalisation among people with diabetes. We included articles published in English from 1980 to 2022. For each study, we determined the most frequent reasons for admission. Studies were assessed for quality using the Newcastle Ottawa quality assessment tool. RESULTS: 6920 research articles were retrieved from the search of all sources. After screening the titles and abstracts of these, we reviewed the full text of 135 papers and finally included data from 42 studies. Admissions among the total diabetes were reported in 25 papers: 5 articles reported type 1 diabetes alone, 10 articles reported type 2 diabetes alone and the remaining 2 articles reported type 1 and type 2 diabetes separately. Among the 25 total and type 2 diabetes studies that reported the distribution of hospitalisations in broad categories, cardiovascular diseases (CVD) were the leading cause of admission in 19/25 (76%) of studies. Among the 19 studies that reported CVD admissions by subcategories, ischaemic or coronary heart disease was the leading subtype of CVD in 58% of studies. The other common causes of admissions were infections, renal disorders, endocrine, nutritional, metabolic and immunity disorders. In people with type 1 diabetes, acute diabetes complications were the leading cause of admission. CONCLUSION: CVD are the leading cause of hospital admission for people with diabetes, with ischaemic or coronary heart disease as the predominant subtype.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".