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Record W2345043591 · doi:10.1097/mog.0000000000000281

Does everyone with inflammatory bowel disease need to be treated with combination therapy?

2016· review· en· W2345043591 on OpenAlexaff
Çharles N. Bernstein

Bibliographic record

VenueCurrent Opinion in Gastroenterology · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineCombination therapyDrugDiseaseInflammatory bowel diseaseConcomitantTumor necrosis factor alphaPharmacotherapyImmunologyInternal medicinePharmacology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article describes the evolution of combination therapy in inflammatory bowel disease and how to determine when combination may not be necessary. RECENT FINDINGS: The combination of an immunosuppressive agent with an antibody to tumor necrosis factor has proven to be more effective than monotherapy with either an immunosuppressive agent or an antibody to tumor necrosis factor alone. The emergence of therapeutic drug monitoring has identified the importance of maintaining adequate circulating drug levels and suppressing antibody formation to the biological agent. The concomitant use of an immunosuppressive agent can help effect this. Although this may be optimal therapy in moderately ill patients. I review why there still remains a role for using monotherapy with immunosuppressive agents, as well as consideration of therapy withdrawal. SUMMARY: Although combination therapy is the treatment of choice in persons with moderate to severe disease or who have lost response to biological monotherapy, there remains a role for monotherapy with immunosuppressive agents. Though the newer biological therapies are mostly not used in combination, this may be a future approach considering their response rates are also correlated with higher drug levels. Many persons with inflammatory bowel disease can remain well on an immunosuppressive agent alone and some can even maintain a longstanding remission off all therapy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.297
Teacher spread0.275 · 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
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

Citations2
Published2016
Admission routes1
Has abstractyes

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