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Record W4381598341 · doi:10.1093/ecco-jcc/jjad106

Lack of Benefit for Early Escalation to Advanced Therapies in Ulcerative Colitis: Critical Appraisal of Current Evidence

2023· article· en· W4381598341 on OpenAlexaff
Johan Burisch, Ekaterina Safroneeva, R Laoun, Christopher Ma

Bibliographic record

VenueJournal of Crohn s and Colitis · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
FundersTillotts Pharma
KeywordsUlcerative colitisCritical appraisalMedicineDe-escalationIntensive care medicineInternal medicineAlternative medicineDiseasePathology

Abstract

fetched live from OpenAlex

Although ulcerative colitis [UC] shares many common pathways and therapeutic options with Crohn's disease [CD], CD patients are four times more likely to undergo surgery 10 years into their disease in the biological era and are more likely to have extraintestinal manifestations than UC patients. Early treatment in CD has been demonstrated to modify the natural history of the disease and potentially delay surgery. Previous reviews on this topic have borrowed their evidence from CD to make UC-specific recommendations. This review highlights the emergence of UC-specific data from larger cohort studies and a comprehensive individual patient data systemic review and meta-analysis to critically appraise evidence on the utility of early escalation to advanced therapies with respect to short-, medium-, and long-term outcomes. In UC, the utility of the early escalation concept for the purposes of changing the natural history, including reducing colectomy and hospitalizations, is not supported by the available data. Data on targeting clinical, biochemical, endoscopic, and histological outcomes are needed to demonstrate that they are meaningful with regard to achieving reductions in hospitalization and surgery, improving quality of life, and minimizing disability. Analyses of different populations of UC patients, such as those with 'relapsing & remitting' disease or with severe or complicated disease course, are urgently needed. The costs and risk/benefit profile of some of the newer advanced therapies should be carefully considered. In this clinical landscape, it appears premature to advocate an indiscriminate 'one size fits all' approach to escalating to advanced therapies early during the course of UC.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.032
GPT teacher head0.364
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2023
Admission routes1
Has abstractyes

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