The Diagnostic Value of Alarm Features for Identifying Types and Stages of Upper Gastrointestinal Malignancies
Bibliographic record
Abstract
BACKGROUND: Upper gastrointestinal (GI) malignancies are an uncommon cause of dyspepsia but of great concern. The aim of this study was to determine the association between alarm features and each type and stage of upper GI malignancies. METHODS: Patients who underwent endoscopy for symptoms of dyspepsia between January 2008 and December 2009 were retrospectively collected. Alarm features studied in this study were dysplasia, body weight loss and GI bleeding. Patients were classified according to the findings of endoscopy and histological reports. RESULTS: A total of 3,926 patients were included in the study, with 82 (2.1%) cases with GI malignancies. The specificity and negative predictive value of alarm features ranged from 93.8% to 99.8%, but the sensitivity and positive predictive value ranged from 11.6% to 29.3%. The only variable with a positive predictive value was dysphagia (66.7%). The patients with esophageal cancers and upper gastric cancers had the highest ratio of alarm features, most body weight loss and dysphagia. There was a positive correlation between alarm features and advanced stages of gastric cancers, with the exception of GI bleeding sign. CONCLUSION: Although alarm features had a low sensitivity in identifying patients with upper GI malignancies, the presence of alarm features did help diagnose esophageal or upper gastric cancer and the sign of GI bleeding for early gastric cancer. In addition, dysphagia and weight loss are associated with higher stages of gastric cancer.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".