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Record W2050649636 · doi:10.1097/mib.0b013e318281f3bb

A Review of Mortality and Surgery in Ulcerative Colitis

2013· review· en· W2050649636 on OpenAlexaffabout
Çharles N. Bernstein, Siew C. Ng, Péter L. Lakatos, Bjørn Moum, Edward V. Loftus

Bibliographic record

VenueInflammatory Bowel Diseases · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineUlcerative colitisColectomyMortality rateInflammatory bowel diseaseCohortPopulationInternal medicineColitisDiseaseSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

Standardized mortality rates in ulcerative colitis (UC) are no different than that in the general population. Patients who are older and have more comorbidities have increased mortality. Emergent colectomy still carries 30-day mortality rates of approximately 5%. In more recent studies, UC surgery rates at 10 years from diagnosis are nearly 3% in Hungary, <10% in referral center studies from Asia, approximately 10% in Norway, the European Cohort Study of Inflammatory Bowel Diseases and Manitoba, Canada, and nearly 17% in Olmsted County, Minnesota. These rates are for the most part lower than reported colectomy rates from studies completed before 1990. Short-term colectomy rates in severe hospitalized UC have remained stable at 27% for several years. Generally, children seem to have higher rates of extensive colitis at diagnosis than adults. There also seems to be higher rates of colectomy in children than in adults (i.e., at least 20% at 10 years), and perhaps, this reflects a higher rate of extensive disease. Acute severe colitis in patients with UC still represents a condition with a high early colectomy rate and a measurable mortality rate.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.310
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations116
Published2013
Admission routes2
Has abstractyes

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