[Algorithm for diagnosis of small intestinal diseases].
Bibliographic record
Abstract
AIM: To review diagnostic approaches in chronic diseases of the small intestine. MATERIAL AND METHODS: A total of 1096 patients with chronic diseases of the small intestine were admitted to the clinic of the Central Research Institute of Gastroenterological Diseases in 1987-2006. RESULTS: Most of the patients (90.5%) had celiac disease, hypolactasia and other types of disaccharidase deficiency, yersiniosis ileitis, Krohn's disease, postresection syndrome of a short small intestine, mesenterial ischemia and endocrine enteropathy. Rare diseases (general variable hypogammaglobulinemia, lymphoma, Wipple's disease and diverticulosis of the small intestine) were diagnosed in 5.8% cases. Primary amyloidosis of the small intestine, eosinophilic gastroenteritis, arteriomesenterial obstruction, primary intestinal pseudoobstruction, hypogammaglobulinemic spru, primary intestinal lymphangiectasia, tuberculosis, total polyposis, Peutz-Eggers and Cronkhite-Canada syndromes, collagenic sprue, erosive-ulcerative jejunoileitis, adenocarcinoma and heavy alpha-chain disease were detected in 3.7% examinees. These diseases were encountered in one to 5 cases for the latest 20 years. CONCLUSION: Clinical diagnosis of small intestinal diseases is based on the syndromes of chronic diarrhea, defective absorption, enteral protein loss, small intestinal obstruction and intestinal hemorrhage. Differential diagnosis of the nosological entities employs x-ray, endoscopic, histological, immunological and other methods. Most of the small intestinal diseases including rare can be diagnosed in any gastroentorological department.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.029 |
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".