Prevalence and surgical outcomes of pediatric intussusception in ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intussusception is the leading cause of pediatric abdominal emergencies worldwide, requiring timely diagnosis and intervention to prevent life-threatening complications. In low-resource settings such as Ethiopia, delayed presentation and limited access to non-surgical management often necessitate surgical intervention. However, comprehensive data on surgical outcomes and complications remain scarce. The aim of this study is to evaluate the epidemiology, clinical presentation, surgical management, and postoperative outcomes of pediatric intussusception in Ethiopia. METHODS: A systematic review was conducted following PRISMA guidelines. Relevant studies were retrieved from PubMed, Scopus, Web of Science, and Google Scholar. Data were extracted on patient demographics, clinical presentation, diagnostic. methods, surgical procedures, complications, and mortality. Quality assessment was performed using the Newcastle-Ottawa Scale. Meta-analysis was conducted using a random-effects model with heterogeneity assessed by I² statistics. Publication bias was evaluated using Egger's test. RESULTS: Seven studies with a total of 672 patients were included. The mean age of affected children was 12 months (95% CI: 11.35, 12.67), with a male predominance (66%). Delayed presentation was common, with a mean time of 3.1 days from symptom onset. The classic triad of symptoms-abdominal pain, bloody stools, and a palpable mass-was present in 52% of cases (I² = 96.24%). Ultrasound was the most common diagnostic tool (74%). The most frequent surgical intervention was manual reduction (62%), followed by bowel resection with anastomosis (35%) and stoma creation. The overall complication rate was 26%, with surgical site infections (15%) being the most common. The pooled mortality rate was 9% (95% CI: 5%, 13%), significantly higher than in high-income countries. Egger's test (p = 0.03) suggested potential publication bias. CONCLUSION: This study found that surgically managed pediatric intussusception in Ethiopia had a high morbidity and mortality rate. These outcomes may reflect delays in presentation, and advanced disease at intervention. The development and implementation of context-specific clinical guidelines could help optimize care and improve survival rates. In addition, further research is needed to evaluate the impact of non-surgical reduction techniques.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".