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Record W4414041802 · doi:10.1055/s-0045-1810737

Understanding gastroenterologist preferences at the time of treatment escalation to first-line advanced therapies in ulcerative colitis: a discrete choice experiment in five European countries

2025· article· en· W4414041802 on OpenAlexaff
S Schreiber, Alissa Walsh, Peter Hur, Laura Panattoni, Brett Hauber, G Gahlon, John Coulter, K Wosik, Joseph C. Cappelleri, Nadya Prood, Ximing Guo, Anthony Buisson

Bibliographic record

VenueZeitschrift für Gastroenterologie · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPfizer (Canada)
Fundersnot available
KeywordsDe-escalationPreferenceMEDLINEAffect (linguistics)Disease

Abstract

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Introduction: As the number of advanced therapies for moderately to severely active ulcerative colitis (UC) increases, it is necessary to understand the factors driving gastroenterologist (GE) choice in escalating patients (pts) from conventional to advanced therapy. Objective: To quantify GE therapy attribute preferences when escalating pts to their first advanced UC therapy. Methodology: We conducted an online, cross-sectional survey using a discrete choice experiment (DCE) design. Attribute and level selection was informed by targeted literature search and formative qualitative research with pts and clinicians. Survey responders were practising GEs experienced in treating pts with moderately to severely active UC, recruited from France, Germany, Italy, Spain and the United Kingdom (UK). Preference weights were estimated using a random parameters logit model for multiple levels of seven attributes: time to symptom improvement, probability of remission at one year, difference between probability of remission and corticosteroid-free remission, five-year risk of malignancy, annual risk of serious infection, annual risk of major adverse cardiovascular events, and mode and frequency of administration. Relative importance (RI) was calculated as the difference in preference weights between the most and least preferred level of each attribute, proportionate to all attribute differences. An extra survey section was added to understand GE treatment and prescribing practices. Results: A total of 397 GEs were included (France n=140; Germany n=40; Italy n=40; Spain n=47; UK n=130). The most common GE-reported barriers to prescribing advanced therapies were concerns about contraindications and risks/side effects from pts (54.9%) and GEs (39.8%), perceived patient concerns about receiving injections or infusions (35.5%) and concerns about cost or insufficient reimbursement (32.2%). All DCE attributes factored into GE treatment decisions (see Table for RI and preference weights). The three most important attributes were probability of remission at one year (RI 48.4%), five-year risk of malignancy (RI 11.4%) and time to symptom improvement (RI 11.1%; [ Table 1 ]). Table 1 Preference weights and RI of attributes influencing advanced UC therapy choice (N=397) Time to symptom improvement Probability of remission at one year Difference between probability of remission and CS-free remission Five-year risk of malignancy Annual risk of serious infection Annual risk of MACE Mode and frequency of administration Attribute RI,a% (95% CI)b 11.1 (8.9, 13.4) 48.4 (45.7, 51.1) 8.0 (6.1, 10.0) 11.4 (9.5, 13.2) 6.7 (4.9, 8.5) 6.8 (5.0, 8.6) 7.5 (5.4, 10.1) Preference weight level (95% CI)c Level 1 2 weeks 0.50 (0.31, 0.69) 20% probability -2.11 (-2.42, -1.81) 0% difference 0.27 (0.13, 0.41) 1/1000 pts 0.49 (0.35, 0.62) 1/100 pts 0.27 (0.14, 0.40) 1/1000 pts 0.27 (0.14, 0.40) Oral pill 1–2 times daily with potential dose change 0.15 (-0.02, 0.33) Level 2 4 weeks 0.22 (0.09, 0.34) 35% probability 0.26 (0.18, 0.34) 5% difference 0.12 (0.03, 0.21) 3/1000 pts -0.04 (-0.13, 0.05) 3/100 pts 0.02 (-0.07, 0.10) 3/1000 pts 0.01 (-0.08, 0.10) Oral pill 1–2 times daily with the same dose throughout 0.23 (0.10, 0.35) Level 3 8 weeks -0.31 (-0.44, -0.18) 45% probability 1.85 (1.65, 2.06) 15% difference -0.39 (-0.48, -0.29) 5/1000 pts -0.45 (-0.54, -0.35) 5/100 pts -0.28 (-0.38, -0.19) 5/1000 pts -0.28 (-0.38, -0.19) Injection every 1–2 weeks 0.01 (-0.12, 0.14) Level 4 12 weeks -0.41 (-0.55, -0.27) N/A N/A N/A N/A N/A Infusion every 4–8 weeks -0.39 (-0.52, -0.26) The DCE model included seven attributes, each with several preference weight levels. a RI is calculated as the difference in preference weights between the most preferred and least preferred level divided by the sum of the differences across all attributes; estimates sum to 100%. b 95% CIs that do not include zero indicate a statistically significant RI of an attribute. All seven attributes were statistically significantly important when selecting an advanced therapy. 95% CIs that do not overlap for pairs of attributes indicate a statistically significant difference in importance between attributes. Probability of remission at one year was statistically significantly more important than all other attributes. c Preference weight levels are effects coded; zero indicates the mean effect across all attribute levels. CI, confidence interval; CS, corticosteroid; DCE, discrete choice experiment; MACE, major adverse cardiovascular events; N, total number of pts; N/A, not applicable; pts, patients; RI, relative importance; UC, ulcerative colitis. Conclusion: All attributes factored into the trade-offs GEs consider when escalating pts with moderately to severely active UC to their first advanced therapy. While risk of side effects was the most stated GE barrier to prescribing advanced therapy, probability of remission outweighed all other DCE attributes. Publication History Article published online: 04 September 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

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)0.023
metaresearch head score (Gemma)0.030
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.225
GPT teacher head0.405
Teacher spread0.179 · 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".

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Published2025
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