The Incidence of Acute Pancreatitis After Device Assisted Enteroscopy: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: The diagnosis of small bowel diseases is challenging and device assisted enteroscopy (DAE) is a technique for visualizing the entire small bowel. DAE is considered as a safe procedure and the reported rate of adverse events associated with DAE in the literature is low. Objective: The present study tried to investigate the actual incidence of AP after DAE with a systematic review and meta-analysis of available relevant studies. Methods: Studies were searched through the PubMed, EMBASE, and Cochrane library databases. The following data were extracted from all eligible studies: author, country, publication year, publication type, study design, type of DAE used, route of DAE, number of patients with AP after DAE, and number of patients with hyperamylasemia after DAE.A random-effects model with RStudio version 4.2.0 was performed in all analyses. Heterogeneity was assessed using the I2 test. The risk of bias was assessed by the Newcastle-Ottawa Scale criteria and the publication bias was assessed by the Egger test. Results: Twenty three studies involving a total of 11145 patients were included in the analysis. The overall, pooled AP rate after DAE was 1% (95% CI:0-1%). There was significant heterogeneity among the studies (I2 = 65%; P < 0.01).The pooled AP rate was 1% (95% CI:0-2 %)in peroral route group. The pooled proportion of patients having hyperamylasemia after DAE was 29% (95% CI: 16-46%).Among the patients who had hyperamylasemia AP were identified in 2% (95% CI: 0-6%) of patients. Conclusion: The incidence of AP after DAE is about 1%. Hyperamylasemia is a common change in the patients undergoing DAE and only 2% of the patients with hyperamylasemia present with AP.
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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.008 | 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".