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Enregistrement 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 sur 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

Notice bibliographique

RevueZeitschrift für Gastroenterologie · 2025
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensPfizer (Canada)
Organismes subventionnairesnon disponible
Mots-clésDe-escalationPreferenceMEDLINEAffect (linguistics)Disease

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,023
score de la tête « metaresearch » (Gemma)0,030
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,121

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0230,030
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,003
Communication savante0,0040,002
Science ouverte0,0010,001
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,225
Tête enseignante GPT0,405
Écart entre enseignants0,179 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentnon

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