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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".