The association between diabetes mellitus and incident infections: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Objective To quantify the association between diabetes and the risk of incident infections by conducting a systematic review and meta-analysis. Research design and methods Two reviewers independently screened articles identified from PubMed, EMBASE, Cochrane Library, IPA, and Web of Science databases. Cohort studies (CS) or case–control studies (CCS) evaluating the incidence of infections in adults with diabetes were included. Infections were classified as: skin and soft tissue, respiratory, blood, genitourinary, head and neck, gastrointestinal, bone, viral, and non-specified infections. Study quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. Summary crude and adjusted OR with 95% CIs were calculated using random effects models, stratified by study design. Heterogeneity was measured using the I2statistic and explored using subgroup analyses. Results A total of 345 (243 CS and 102 CCS) studies were included. Combining adjusted results from all CS, diabetes was associated with an increased incidence of skin (OR 1.94, 95% CI 1.78 to 2.12), respiratory (OR 1.35, 95% CI 1.28 to 1.43), blood (OR 1.72, 95% CI 1.48 to 2.00), genitourinary (OR 1.61, 95% CI 1.42 to 1.82), head and neck (OR 1.17, 95% CI 1.13 to 1.22), gastrointestinal (OR 1.48, 95% CI 1.40 to 1.57), viral (OR 1.29, 95% CI 1.13 to 1.46), and non-specified (OR 1.84, 95% CI 1.66 to 2.04) infections. A stronger association was observed among CCS: skin (OR 2.64, 95% CI 2.20 to 3.17), respiratory (OR 1.62, 95% CI 1.37 to 1.92), blood (OR 2.40, 95% CI 1.68 to 3.42), genitourinary (OR 2.59, 95% CI 1.60 to 4.17), gastrointestinal (OR 3.61, 95% CI 2.94 to 4.43), and non-specified (OR 3.53, 95% CI 2.62 to 4.75). Conclusion Diabetes is associated with an increased risk of multiple types of infections. A high degree of heterogeneity was observed; however, subgroup analysis decreased the amount of heterogeneity within most groups. Results were generally consistent across types of infections.
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".