Complementary Computed Tomography to Inconclusive Ultrasonography in Children with Suspected Acute Appendicitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction The optimal diagnostic pathway for pediatric acute appendicitis (AA) following an inconclusive or negative ultrasonography (US) is poorly defined, leading to debate over subsequent computed tomography (CT) use. This systematic review and meta-analysis compared negative appendectomy rates in children managed with a US-only pathway vs a pathway involving CT after a non-diagnostic initial US. Methods Following PRISMA guidelines (PROSPERO: CRD42024568560), we systematically searched 6 databases, including PubMed and Embase, through July 2024 for longitudinal studies comparing the 2 diagnostic pathways. Two reviewers independently selected studies and extracted data. Risk of bias in included studies was assessed using the ROBINS-I and Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the GRADE framework. A fixed-effects meta-analysis was performed to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs). Results The pooled analysis demonstrated that a US-only pathway was associated with significantly lower odds of negative appendectomy compared to the US-followed-by-CT pathway (OR 0.44; 95% CI 0.21-0.90; P = 0.02). This protective association was even more pronounced in the subgroup of patients with an initial inconclusive US (OR 0.22; 95% CI 0.05-0.89; P = 0.03). Conclusion In children with suspected AA after a non-diagnostic US, a US-only pathway is associated with a significantly lower rate of negative appendectomy. These findings support strategies that prioritize clinical re-evaluation and repeat US to reduce unnecessary surgeries and radiation exposure. However, given the low certainty of evidence, clinical judgment remains paramount.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.004 | 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".