Risk factors for the readmission of patients with diabetic ketoacidosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose: This study aimed to identify risk factors associated with the readmission of patients with diabetic ketoacidosis (DKA) through a systematic review and meta-analysis. Methods: A systematic literature review was conducted in accordance with the PRISMA guidelines. Relevant studies were retrieved from international databases (PubMed, EMBASE, Cochrane Library, CINAHL, PsycINFO, and Web of Science) and Korean databases (RISS, KoreaMed, KMbase, KISS, and DBpia). Study quality was evaluated using the Newcastle-Ottawa Scale. Meta-analysis was performed using a random-effects model with the Hartung-Knapp-Sidik-Jonkman adjustment to account for the limited number of studies and heterogeneity. Results: Fifteen studies were included in the review, and eight were eligible for meta-analysis. From the systematic review, 21 risk factors for DKA readmission were identified and categorized into five domains: demographic, socioeconomic, diabetes-related, comorbidity, and health-behavioral factors. In the meta-analysis, significant risk factors included low income, psychiatric disorders, and discharge against medical advice. Conclusion: This study demonstrates that DKA readmissions result from the complex interplay of multiple clinical and social factors. By identifying these risk factors and suggesting risk-stratification criteria, the findings may support the development of tailored interventions, such as self-management education, integrated mental health care, structured discharge planning, and coordinated post-discharge follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.048 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.003 | 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".