In Patients Referred for Investigation Because Computed Tomography Suggests Thickened Gastric Folds, Endoscopic Ultrasound Is Superfluous If Gastroscopy Is Normal
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Endoscopic ultrasound (EUS) is often requested in patients in whom computed tomography (CT) shows gastric wall thickening. It is unclear if EUS is useful if upper endoscopy is normal. The aim of this study was to prospectively compare the yield of upper endoscopy and EUS for this indication. METHODS: All patients referred for endoscopic ultrasound because of thickened gastric folds on CT from May 2001 and June 2003 were included. A single physician, questioned, examined, and performed upper endoscopy followed by EUS in all patients. Data were recorded prospectively. The main outcome measures were: upper endoscopy and EUS findings and predictors of abnormal EUS. RESULTS: Sixty-nine patients were enrolled. The average age was 57.9, 49% were male, 51% were asymptomatic, 57% had normal upper endoscopy, and 70% had normal EUS. If upper endoscopy was abnormal, EUS was abnormal in 70% of cases (95% CI 62%-78%). If upper endoscopy was normal, the EUS was normal in 100% of cases (95% CI 92%-100%). Multivariate analysis revealed that neither age, gender, presence of abdominal symptoms nor alarm symptoms predicted abnormal EUS. CONCLUSIONS: When CT shows gastric wall thickening: (a) Nnormal upper endoscopy is strongly associated with normal EUS; (b) abnormal upper endoscopy is associated with abnormal EUS in 70% of cases; (c) clinical variables such as age, sex, and the presence of symptoms do not predict or increase the likelihood of abnormal EUS. Therefore, in patients with thickened gastric wall on CT, upper endoscopy should be used to select patients for EUS.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".