Video capsule endoscopy versus computed tomography enterography in assessing suspected small bowel bleeding: a systematic review and diagnostic test accuracy meta-analysis
Bibliographic record
Abstract
Both computed tomography enterography (CTE) and video capsule endoscopy (VCE) are used in identifying small intestinal pathology in patients with suspected small bowel bleeding (SSBB) following normal upper gastrointestinal endoscopy and colonoscopy. Evidence of the comparative accuracy of these two modalities is crucial for clinical and healthcare decision-making. Comprehensive electronic searches were performed for studies on CTE and/or VCE with reference standard(s). Study selection, data extraction and quality assessment were completed by two authors independently. The QUADAS-2 and QUADAS-C tools were used to assess risk of bias, and applicability. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, positive and negative likelihood ratios. Twenty-five studies involving 1986 patients with SSBB were included. Four of these were head-to-head comparison of CTE and VCE. Overall, VCE provided significantly higher sensitivity of 0.74 (95% CI: 0.61-0.83) versus 0.47 (95% CI: 0.32-0.62) for CTE, while CTE showed significantly higher specificity of 0.94 (95% CI: 0.64-0.99) versus 0.53 (95% CI: .36-0.69) for VCE. The positive likelihood ratio of CTE was 7.36 (95% CI: 0.97-56.01) versus 1.58 (95% CI: 1.15-2.15) for VCE and the negative likelihood ratio was 0.49 (95% CI: 0.33-0.72) for VCE versus 0.56 (0.40-0.79) for CTE. A secondary analysis of only head-to-head comparative studies gave results that were similar to the main analysis. Certainty of evidence was moderate. Neither VCE nor CTE is a perfect test for identifying etiology of SSBB in small intestine. VCE was more sensitive while CTE was more specific. Clinicians should choose the appropriate modality depending on whether better sensitivity or specificity is required in each clinical scenario.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".