Diagnostic capabilities of high-definition white light endoscopy for the diagnosis of gastric intestinal metaplasia and correlation with histologic and clinical data
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was the evaluation of the diagnostic accuracy of a specific high-definition white light endoscopy (HD-WLE) system for the optical recognition of intestinal metaplasia (IM) and the assessment of its correlation with histologic and clinical data. METHODS: A total of 234 patients undergoing upper gastrointestinal endoscopy in an outpatient endoscopy suite for various indications were prospectively enrolled in this cross-sectional study. Gastric IM was diagnosed on the basis of three mucosal patterns identified using HD-WLE in a per-patient analysis. Histological evaluation was used as the gold standard, and special staining was conducted for subtyping of IM. Main outcome measurements were sensitivity, specificity, and likelihood ratio of HD-WLE and secondary associations with histologic and clinical data. RESULTS: IM was found in 63/234 (27%) patients and low-grade dysplasia in 6/63 patients (9.5%). Sensitivity, specificity, accuracy, and likelihood ratio of all mucosal patterns were 74.6, 94, 88% and 13, respectively. All clinically significant type III IM and dysplasia lesions were endoscopically detected. All nonvisible lesions were of types I and II with mild grade and no dysplasia. Ten patients were considered false positives and the lesions were associated with severe inflammation and antralization. CONCLUSION: The specific HD-WLE system showed satisfactory accuracy and high specificity during real-time, routine endoscopy practice. Specific mucosal patterns were correlated with level and grade of lesions. The sensitivity of the system is even higher when only clinically significant IM lesions are considered.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".