30‐day readmission rates among upper gastrointestinal bleeds: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background and Aim Upper gastrointestinal bleeding (UGIB) is a common emergency, with high rates of hospitalization and in‐patient mortality compared to other gastrointestinal diseases. Despite readmission rates being a common quality metric, little data are available for UGIBs. This study aimed to determine readmission rates for patients discharged following an UGIB. Methods Adhering to PRISMA guidelines, MEDLINE, Embase, CENTRAL, and Web of Science were searched to October 16, 2021. Randomized and non‐randomized studies that reported hospital readmission for patients following an UGIB were included. Abstract screening, data extraction, and quality assessment were conducted in duplicate. A random‐effects meta‐analysis was performed, with statistical heterogeneity measured using I2. The GRADE framework, with a modified Downs and Black tool, was used to determine certainty of evidence. Results Seventy studies were included of 1847 screened abstracted, with moderate interrater reliability. Within these studies, 4 292 714 patients were analyzed with a mean age of 66.6 years, and 54.7% male. UGIB had a 30‐day all‐cause readmission rate of 17.4% (95% confidence interval [CI] 16.7–18.2%), stratification revealed a higher rate for variceal UGIB [19.6% (95% CI 17.6–21.5%)] than non‐variceal [16.8% (95% CI 16.0–17.5%)]. Only one third were readmitted due to recurrent UGIB (4.8% [95% CI 3.1–6.4%]). UGIB due to peptic ulcer bleeding had the lowest 30‐day readmission rate [6.9% (95% CI 3.8–10.0%)]. Certainty of evidence was low or very low for all outcomes. Conclusions Almost one in five patients discharged after an UGIB are readmitted within 30 days. These data should prompt clinicians to reflect on their own practice to identify areas of strength or improvement.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".