Gastric cystica profunda: Another indication for minimally invasive endoscopic resection techniques?
Bibliographic record
Abstract
Gastric cancer presents a significant global health burden, as it is the fifth most common malignancy and fourth leading cause of cancer mortality worldwide. Variations in incidence rates across regions underscores the multifactorial etiology of this disease. The overall 5-year survival rate remains low despite advances in its diagnosis and treatment. Although surgical gastrectomy was previously standard-of-care, endoscopic resection techniques, including endoscopic mucosal resection and endoscopic submucosal dissection (ESD) have emerged as effective alternatives for early lesions. Compared to surgical resection, endoscopic resection techniques have comparable 5-year survival rates, reduced treatment-related adverse events, shorter hospital stays and lower costs. ESD also enables en bloc resection, thus affording organ-sparing curative endoscopic resection for early cancers. In this editorial, we comment on the recent publication by Geng et al regarding gastric cystica profunda (GCP). GCP is a rare gastric pseudotumour with the potential for malignant progression. GCP presents a diagnostic challenge due to its nonspecific clinical manifestations and varied endoscopic appearance. There are several gaps in the literature regarding the diagnosis and management of GCP which warrants further research to standardize patient management. Advances in endoscopic resection techniques offer promising avenues for GCP and early gastric cancers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".