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Expert round-table Constipation: diagnosis, treatment, risks

2020· article· en· W3098049903 on OpenAlexaboutno aff
Article Editorial

Bibliographic record

VenueMeditsinskiy sovet = Medical Council · 2020
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConstipationFamily medicineDiseasePopulationQuarter (Canadian coin)EpidemiologyGeneral surgeryHealth careGerontologyInternal medicineHistoryEnvironmental health

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.260
GPT teacher head0.342
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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