Cytologic diagnosis of gastric submucosal lesions by endoscopic ultrasound-guided fine-needle aspiration: A single center experience in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND AND AIMS: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) sampling has become standard practice for the diagnosis of submucosal gastrointestinal (GI) lesions. The aim of this study was to determine the utility of EUS-guided FNA cytology in the diagnosis of deeply seated gastric mass lesions. MATERIALS AND METHODS: Thirteen patients with deeply seated gastric mass lesions were diagnosed by EUS-FNA. Adequate cytology material was present in all cases. Cell blocks were available in 10 cases. Surgical resections were performed in 8 cases. Immunohistochemical (IHC) studies were done on cell blocks in 9 cases and on 6 resected specimens. Seven cases has proved to be GI stromal tumors (GIST), in four of them, cell blocks were available, and resection for GIST was performed in 5 cases. IHC stains that were performed in cytology, as well as resection specimens, revealed similar results in each patient. CONCLUSION: EUS-FNA cytology, when combined with a histologic assessment of cell blocks provides accurate and efficient tissue diagnosis of a wide variety of deeply seated gastric mass lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".