Bibliographic record
Abstract
According to epidemiological data, nearly a quarter of the adult population in developed countries suffers from constipation. Due to a significant decrease in quality of life, it represents a serious medical and social problem. Constipation occurs more frequently in the elderly, but both children and adults face this problem. This disease is often caused by the features of modern life: hypodynamia, diet violations, a fiber- and water-depleted diet, frequent stress, etc. Unfortunately, despite the high prevalence of the disease, patients do not seek immediate medical advice. We will discuss this urgent problem and look for new efficient solutions of it with the leading experts: Gastroenterologist Yuri A. Kucheryavy, Cand. of Sci. (Med.), Associate Professor of Department of Propedeutics of Internal Diseases and Gastroenterology, A.I.Yevdokimov Moscow State University of Medicine and Dentistry, Chief Gastroenterologist of Central Healthcare Directorate – Branch of Joint Stock Company “Russian Railways”; Endoscopist Ekaterina V. Ivanova, Dr. of Sci. (Med.), Chief Researcher, Research Laboratory of Surgical Gastroenterology and Endoscopy, Head of Endoscopy Department of Petrovskie Vorota Medical Center, and Coloproctologist Daniil R. Markaryan, Cand. of Sci. (Med.), Senior Researcher, M.V. Lomonosov Moscow State University Clinic.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.030 |
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".