Clinicopathological Features and the Prevalence of Oxyntic Gland Neoplasm: A Single-center Retrospective Study
Bibliographic record
Abstract
Objective We explored the clinicopathological characteristics and disease frequency of oxyntic gland neoplasms (OGNs). Methods We retrospectively evaluated the data of patients pathologically diagnosed with OGN at an internal medicine clinic. Patients A total of 13,240 upper gastrointestinal endoscopies were performed on 7,488 patients between December 1, 2017, and March 31, 2021. Results We identified 27 patients with 30 histopathologically confirmed OGNs, yielding a disease frequency of 0.36% (27/7,488). Furthermore, multiple simultaneous lesions were found in 3 of 27 patients (11%). One (3.3%) of the 30 lesions was present in the antrum, whereas the remaining lesions occurred in the body of the stomach. Nine (33%) of the 27 patients had no history of Helicobacter pylori infection, whereas the remaining 18 (67%) were either currently or had been previously infected. Nevertheless, 27/30 lesions (90%) still occurred in non-atrophied regions. After endoscopic treatment, a histopathological examination of the resected specimens revealed submucosal infiltration in 8 (44%) of the 18 lesions; however, none of the lesions showed submucosal desmoplasia. For all patients with submucosal involvement, only observation was performed. There were no recurrent lesions found on follow-up. Conclusion The period prevalence of OGN was 0.36%, which is much higher than previously reported. The discovery of a small submucosal appearing lesion with a faded yellow or white color and dilated microvasculature, especially in a non-atrophic area of the stomach, should raise suspicion for an OGN, which can be endoscopically managed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".