Influence of diabetes on short-term mortality of patients with acute type A aortic dissection: a meta-analysis
Bibliographic record
Abstract
Acute type A aortic dissection (ATAAD) is a life-threatening cardiovascular emergency with substantial early mortality. Whether diabetes mellitus (DM) independently influences short-term mortality in ATAAD remains uncertain. We conducted a meta-analysis to synthesize the available data. PubMed, Embase, and Web of Science were searched from inception to June 22, 2025, for longitudinal studies reporting short-term mortality (in-hospital or 30-day) in ATAAD patients with and without DM. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a random-effects model accounting for the influence of heterogeneity. Subgroup, sensitivity, and meta-regression analyses explored heterogeneity. The study quality was assessed with the Newcastle–Ottawa Scale (NOS). Twenty-one retrospective cohort studies involving 19,291 patients were included. DM was associated with a modestly increased short-term mortality risk (OR = 1.48; 95% CI 1.14 to 1.91; p = 0.003; I 2 = 44%). The results were consistent in sensitivity analyses restricted to surgical patients (OR = 1.52; 95% CI 1.18 to 1.96; p = 0.001). Subgroup analyses showed no significant differences by region, follow-up duration, analytic model, or NOS score (all p for subgroup differences > 0.05). Meta-regression analysis showed that sample size was negatively correlated with the association (coefficient = − 0.000074, p = 0.02), which fully explained the source of heterogeneity (adjusted R 2 = 100%). This meta-analysis of retrospective studies suggests that DM may be associated with higher short-term mortality in ATAAD. Given the observational nature of the evidence, further prospective studies are needed to clarify this relationship.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.063 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".