Advanced Imaging Technologies for the Detection of Dysplasia and Early Cancer in Barrett Esophagus
Bibliographic record
Abstract
Advanced esophageal adenocarcinomas arising from Barrett esophagus (BE) are tumors with an increasing incidence and poor prognosis. The aim of endoscopic surveillance of BE is to detect dysplasia, particularly high-grade dysplasia and intramucosal cancers that can subsequently be treated endoscopically before progression to invasive cancer with lymph node metastases. Current surveillance practice standards require the collection of random 4-quadrant biopsy specimens over every 1 to 2 cm of BE (Seattle protocol) to detect dysplasia with the assistance of white light endoscopy, in addition to performing targeted biopsies of recognizable lesions. This approach is labor-intensive but should currently be considered state of the art. Chromoendoscopy, virtual chromoendoscopy (e.g., narrow band imaging), and confocal laser endomicroscopy, in addition to high-definition standard endoscopy, might increase the diagnostic yield for the detection of dysplastic lesions. Until these modalities have been demonstrated to enhance efficiency or cost effectiveness, the standard protocol will remain careful examination using conventional off the shelf high-resolution endoscopes, combined with as longer inspection time which is associated with increased detection of dysplasia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".