Biomarkers in the Molecular Pathogenesis of Esophageal (Barrett) Adenocarcinoma
Bibliographic record
Abstract
Since the early 1970s, a dramatic change has occurred in the epidemiology of esophageal malignancy in both North America and Europe: the incidence of adenocarcinomas of the lower esophagus and esophagogastric junction is increasing. Several lifestyle factors are implicated in this change, including gastroesophageal reflux disease (GERD). Primary esophageal adenocarcinomas are thought to arise from Barrett esophagus, an acquired condition in which the normal esophageal squamous epithelium is replaced by a specialized metaplastic columnar-cell-lined epithelium.Today, gerd is recognized as an important risk factor in Barrett esophagus. Progression of Barrett esophagus to invasive adenocarcinoma is reflected histologically by the metaplasia-dysplasia-carcinoma sequence. Although several molecular alterations associated with progression of Barrett esophagus to invasive adenocarcinoma have been identified, relatively few will ultimately have clinical application. Currently, the histologic finding of high-grade dysplasia remains the most reliable predictor of progression to invasive esophageal adenocarcinoma. However other promising molecular biomarkers include aneuploidy; 17p loss of heterozygosity, which implicates the TP53 tumour suppressor gene; cyclin D1 protein overexpression; and p16 alterations. It is anticipated that models incorporating combinations of objective scores of sociodemographic and lifestyle risk factors (that is, age, sex, body mass index), severity of gerd, endoscopic and histologic findings, and a panel of biomarkers will be developed to better identify patients with Barrett esophagus at increased risk for malignant progression, leading to more rational endoscopic surveillance and screening programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".