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Record W2967907952 · doi:10.3748/wjg.v25.i30.4158

Elderly patients with inflammatory bowel disease: Updated review of the therapeutic landscape

2019· review· en· W2967907952 on OpenAlexaff
Jean‐Frédéric LeBlanc, Daniel Wiseman, Péter L. Lakatos, Talat Bessissow

Bibliographic record

VenueWorld Journal of Gastroenterology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill University Health Centre
FundersPfizer
KeywordsMedicineInflammatory bowel diseaseMedical prescriptionQuality of life (healthcare)Randomized controlled trialPopulationDiseaseClinical trialIntensive care medicineRetrospective cohort studyUlcerative colitisInternal medicinePharmacologyEnvironmental health

Abstract

fetched live from OpenAlex

High-quality data remains scarce in terms of optimal management strategies in the elderly inflammatory bowel disease (IBD) population. Indeed, available trials have been mostly retrospective, of small sample size, likely owing to under-representation of such a population in the major randomized controlled trials. However, in the last five years, there has been a steady increase in the number of published trials, helping clarify the estimated benefits and toxicity of the existing IBD armamentarium. In the Everhov trial, prescription strategies were recorded over an average follow-up of 4.2 years. A minority of elderly IBD patients (1%-3%) were treated with biologics within the five years following diagnosis, whilst almost a quarter of these patients were receiving corticosteroid therapy at year five of follow-up, despite its multiple toxicities. The low use of biologic agents in real-life settings likely stems from limited data suggesting lower efficacy and higher toxicity. This minireview will aim to highlight current outcome measurements as it portends the elderly IBD patient, as well as summarize the available therapeutic strategies in view of a growing body of evidence.

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 score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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

Citations40
Published2019
Admission routes1
Has abstractyes

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