Small bowel polyps and tumours: endoscopic detection and treatment by double‐balloon enteroscopy
Bibliographic record
Abstract
BACKGROUND: Double-balloon enteroscopy has allowed us not only to inspect deeply the small bowel but also to carry out interventions for diseases of the small bowel. AIM: To evaluate the utility of double-balloon enteroscopy for the diagnosis and therapy of these lesions. METHODS: All patients undergoing double-balloon enteroscopy for evaluation of small bowel polyps and tumours during a 3.75-year period at a university referral hospital were studied. The types of polyps and tumours as well as endoscopic technique of removal, surgery and complications were documented. RESULTS: The incidence of small bowel polyps and tumours in-patients undergoing DBE was 9.6%. A total of 40 double-balloon enteroscopy procedures were performed in 29 patients [13 female (44.8%), mean age 51 years, range 22-74]. The following lesions were found most frequently: adenomas in familial adenomatous polyposis syndrome, n = 8; hamartomas, n = 4 (Peutz-Jeghers and Cronkhite Canada syndromes), jejunal adenocarcinoma n = 5, neuroendocrine tumour n = 4 and others n = 6. CONCLUSIONS: The incidence of small bowel tumours in those in-patients who were undergoing double-balloon enteroscopy was 10%. Double-balloon enteroscopy is useful for the diagnosis and treatment of small bowel polyps and tumours.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".