A161 SYMPTOMATIC GASTRIC SCHWANNOMA DIAGNOSED WITH ENDOSCOPIC ULTRASOUND AND FINE NEEDLE BIOPSY: A CASE REPORT
Bibliographic record
Abstract
Abstract Background Gastric schwannomas are exceedingly rare neoplasms that account for 0.2% of all gastric masses. They belong to a group of peripheral nerve sheath tumors, which are typically slow-growing, benign lesions that also include neurofibromas and perineuriomas. These neoplasms are often found incidentally on endoscopy as subepithelial lesions. Purpose We aim to outline the presentation and differential diagnosis of gastric peripheral nerve sheath tumors. Method We present the case of a 44 year old female with heartburn and dyspepsia who was referred for esophagogastroduodenoscopy, which incidentally found a subepithelial lesion on the greater curve of the stomach. She was then referred for EUS-guided fine needle biopsy of the mass, and the pathology was initially reported as a neurofibroma. Eventually, the lesion was surgically removed with the final pathology report stating that this was a gastric Schwannoma. Both specimens had immunostains positive for S-100 and negative for CD34, desmin, DOG1 and cKit. Result(s) While gastric schwannomas are typically benign, they can still cause abdominal pain, gastrointestinal bleeding, and even gastric outlet obstruction and intussusception. The differential diagnosis of gastric schwannoma includes other peripheral nerve sheath tumors, as well as malignant subepithelial lesions such as gastrointestinal stromal tumors or gastric lymphoma. Conclusion(s) Gastric schwannoma should always be considered when evaluating subepithelial gastric lesions, and EUS with histologic sampling should be pursued to diagnose and guide the management of these lesions. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".