Diagnostic Characteristics of Given Video Capsule Endoscopy in Diagnosis of Celiac Disease: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: In the view of small sample sizes of the studies published so far, the value of video capsule endoscopy (VCE) in diagnosing celiac disease (CD) is yet to be determined. The aim of this work was to systemically determine the overall diagnostic characteristics of VCE in diagnosing noncomplicated CD, compared to the gold standard, using meta-analysis. PATIENTS AND METHODS: An extensive literature search was performed looking for prospective, controlled trials, with investigators blinded to results of the pathology of small-bowel biopsies. Two independent authors performed data extraction and assessment of the methodologic quality of each trial. Diagnostic characteristics of each trial were collected, and pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratios were computed. Description of complications and costs was included, if reported. RESULTS: A total of three studies met the inclusion criteria (n = 107; 63 with CD and 44 without). The overall pooled VCE sensitivity was 83% (95% confidence interval [CI] = 71-90%) and specificity was 98% (95% CI = 88-99.6%). No major complications were reported. The costs were mentioned only in one study. CONCLUSIONS: The overall diagnostic characteristics of VCE, when used to diagnose celiac disease, though good with an experienced eye, could not justify the routine use of VCE as an alternative to the pathology of small-bowel biopsies. More studies are needed with proper cost-benefit analysis.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".