Taking the Next Steps in Endoscopic Visual Assessment of Barrett’s Esophagus: A Pilot Study
Bibliographic record
Abstract
PURPOSE: Patients with Barrett's esophagus (BE) undergo surveillance endoscopies to assess for pre-cancerous changes. We developed a simple endoscopic classification method for predicting non-dysplastic BE (NDBE), low-grade dysplasia (LGD)/indefinite for dysplasia (ID) and high-grade dysplasia (HGD)/early esophageal adenocarcinoma (EAC). PATIENTS AND METHODS: Twenty-two patients with BE underwent endoscopy using the PENTAX Medical MagniView gastroscope and OPTIVISTA processor. Sixty-six video-still images were analyzed to characterize the microsurface, microvasculature and the presence of a demarcation line. Class A was characterized by regular microvascular and microsurface patterns and absence of a demarcation line, class B by changes in the microvascular and/or microsurface patterns compared to the background mucosa with presence of a demarcation line, and class C by irregular microvascular and/or irregular microsurface patterns with presence of a demarcation line. RESULTS: Of the class A images, 97.9% were NDBE. For class B, 69.2% were LGD/ID and 30.8% NDBE. One hundred percent of the class C samples were HGD/EAC. The sensitivity of our classification system was 93.8%, specificity 92%, positive predictive value 78.9%, negative predictive value 97.9% and an accuracy 92.4%. CONCLUSION: In this study, we developed a simple classification system for the prediction of NDBE, LGD/ID and HGD/EAC. Its real-time clinical applicability will be validated prospectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".