P0695 Quantifying the benefit-risk trade-offs adult patients and providers are willing to make when considering advanced therapies for moderate to severe Ulcerative Colitis: A discrete choice experiment
Notice bibliographique
Résumé
Abstract Background As advanced therapy options for ulcerative colitis (UC) increase, it is important to understand the benefit-risk trade-offs patients and providers are willing to make to better inform shared decision making. Methods We used a cross-sectional survey with a discrete choice experiment (DCE) design to quantify advanced UC therapy preferences in patients with moderate to severe UC and practicing providers from the US, UK, France, Germany, Italy and Spain. Respondents chose between hypothetical combinations of attributes that were informed by a targeted literature search and formative qualitative research with patients and clinicians. Attributes comprised of time until symptom improvement, probability of remission at one year, probability of corticosteroid (CS)-free remission at one year, annual risk of serious infection, five-year risk of cancer and annual risk of major adverse cardiovascular event (MACE). Relative importance (RI; scaled 0–100%) for each attribute was calculated as the difference in mean preference weights between the most and least preferred level divided by the sum of the differences; for RI, remission attributes were combined. The maximum-acceptable risk (MAR) for each risk attribute, and the simultaneous MAR thresholds (SMARTs) for all risk attributes considered jointly, in exchange for a 10-percentage point increase in probability of remission at one year were estimated. Results For treatment choices, combined remission and CS-free remission at one year was significantly prioritised by patients (N=557; RI 40.1%) and providers (N=500; RI 51.1%), followed by five-year risk of cancer (RI 31.7% and 25.7%, respectively) (Table). Time to symptom improvement was significantly preferred vs annual risk of MACE and serious infection for providers, but not patients (Table). For a 10-percentage point increase in probability of remission at one year, providers had a higher MAR for five-year risk of cancer vs patients (4.0% vs 2.5%); for the other two risk attributes, reported MARs were beyond the DCE-included limits (Figure). For a 10-percentage point increase in the probability of remission at one year when annual risk of serious infection was 1% or 3%, providers were willing to jointly accept higher annual risk of MACE and five-year risk of cancer vs patients (Figure). Conclusion Combined probability of remission and CS-free remission at one year had the strongest influence on treatment choice. Compared with patients, providers on average placed greater importance on benefits and had a higher tolerance of risks, particularly 5-year risk of cancer. These findings highlight the importance of shared clinical decision making. References Pfizer’s generative artificial intelligence tool MAIA was used to assist production of the abstract first draft. Authors reviewed/edited and take responsibility for the content.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».