Screening for Esophagitis and Barrett's Esophagus With Wireless Esophageal Capsule Endoscopy: A Multicenter Prospective Trial in Patients With Reflux Symptoms
Bibliographic record
Abstract
BACKGROUND AND AIM: Esophageal capsule endoscopy (ECE) is a new technology that allows noninvasive investigation of the esophagus. Our aim was to evaluate prospectively the diagnostic yield of ECE in patients with chronic reflux symptoms. PATIENTS AND METHODS: Eighty-nine patients (40 men, mean age 54 yr) referred to five endoscopic centers for esophagogastroduodenoscopy (EGD) were enrolled. Patients first underwent ECE, then EGD; endoscopists who performed the EGD were blind to the ECE data that were interpreted separately by two independent readers. The Los Angeles, Prague, and Montreal classification systems were used to describe endoscopic findings. RESULTS: Seventy-seven patients completed the study. Esophagitis and endoscopically suspected esophageal metaplasia (ESEM) were present in 24 and 10 patients, respectively. Columnar lining was histologically confirmed in seven patients (3 with specialized intestinal metaplasia and 4 with gastric metaplasia). The kappa values for interobserver agreement regarding the diagnosis of esophagitis and ESEM were 0.67 (0.49-0.85) and 0.49 (0.17-0.81), respectively. The diagnostic yields of ECE to detect esophagitis and ESEM were as follows: sensitivity 79% and 60%, specificity 94% and 100%, positive predictive value (PPV) 83% and 100%, negative predictive value (NPV) 92% and 95%, respectively. CONCLUSION: As a screening tool in patients with reflux symptoms, ECE has an excellent specificity and NPV for the diagnosis of esophagitis and ESEM. However, its sensitivity for the diagnosis of ESEM is not optimal. Further studies are necessary to improve the procedure, and to compare the cost-effectiveness of strategies using ECE or EGD.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".