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Record W4318580112 · doi:10.1093/ecco-jcc/jjac190.0990

P860 The Costs of Inflammatory Bowel Diseases in High-Income Settings

2023· article· en· W4318580112 on OpenAlexaff
Johan Burisch, Mirabella Zhao, Shmuel Odes, Peter De Cruz, Séverine Vermeire, Çharles N. Bernstein, Gilaad G. Kaplan, Dana Ďuricová, Dan Greenberg, Hans Olav Melberg, Masatoshi Watanabe, Hanse Ahn, Laura E. Targownik, Valérie Pittet, V Annese, K Park, K H Konstantinos H, Marte Lie Høivik, Željko Krznarić, María Chaparro, Edward V. Loftus, Péter L. Lakatos, Javier P. Gisbert, Willem A. Bemelman, B Moum, Richard B. Gearry, Michael D. Kappelman, Adam Hart, Jane M. Andrews, Susanna S. Ng, R. D’Incà, Pia Munkholm

Bibliographic record

VenueJournal of Crohn s and Colitis · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill UniversityUniversity of TorontoUniversity of CalgaryMount Sinai HospitalUniversity of Manitoba
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseHealth careCommissionUlcerative colitisDisease burdenDiseaseFamily medicineEnvironmental healthBusinessEconomic growthPathologyFinance

Abstract

fetched live from OpenAlex

Abstract Background Crohn’s disease (CD) and ulcerative colitis (UC), together known as inflammatory bowel diseases (IBD), affect approximately seven million people globally. The continuous rise in IBD prevalence and the aging of populations will inevitably lead to an increasing use of health care resources. In parallel with these trends, continuing innovations in IBD therapeutics, diagnostics, and preventatives are creating more options for IBD patients seeking to reduce their disease burden. These trends pose an imminent burden on health care systems and require that we identify modifiable cost drivers in IBD care and develop strategies for delivering equitable and affordable care for all patients. Methods This Lancet Gastroenterology & Hepatology Commission, consisting of a diverse faculty of health care professionals with expertise in the field of IBD and health economists, was formed to deliver an extensive summary of the literature and discuss key topics on the costs and cost-effectiveness of treating IBD in the present, and how this is likely to look in the future. Furthermore, it offers suggestions for how to deliver affordable IBD care in the coming years. The report’s focus was on high-income countries in Europe, North America, Australia and New Zealand, and Asia. Results The Commission drew together a wide range of expertise to discuss the current costs of IBD care, the drivers of increasing costs, as well as how to deliver affordable care for IBD in the future. The key conclusions are that (i) increases in health care costs must be evaluated against improved disease control and reductions in indirect costs, and (ii) that overarching systems for data interoperability, registries, and big data approaches must be established for continuous assessment of effectiveness, costs, and cost-effectiveness of care. International collaborations should be sought in order to evaluate novel models of care (such as value-based health care, including integrated health care and participatory health care models), as well as to improve the education and training of clinicians, patients, and policymakers. Measures to reduce the costs of IBD care are summarised in Table 1. Conclusion Increases in health care costs must be evaluated against improved disease control and reductions in indirect costs. Evaluations should be systematically aligned between countries and regions. Detailed analysis of the current epidemiology and the likely effects of changing IBD management on disease course and socioeconomic outcomes is essential; and will become even more imperative in the era of precision medicine, where complex biotechnologies will require expensive analyses, highly skilled personnel, and drug development for what may sometimes be relatively small patient groups.

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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.034
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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