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Enregistrement W4323351499 · doi:10.1093/jcag/gwac036.216

A216 BOWEL URGENCY COMMUNICATION GAP BETWEEN HEALTH CARE PROFESSIONALS AND PATIENTS WITH ULCERATIVE COLITIS IN THE US AND EUROPE: COMMUNICATING NEEDS AND FEATURES OF IBD EXPERIENCES (CONFIDE) SURVEY

2023· article· en· W4323351499 sur OpenAlexaff
Simon Travis, Alison Potts Bleakman, Donald B. Rubin, Marla C. Dubinsky, Remo Panaccione, T Hibi, C Kayhan, E Flynn, Christophe Sapin, Christian Atkinson, Stefanie Schreiber, J. B. Jones

Notice bibliographique

RevueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueCeliac Disease Research and Management
Établissements canadiensDalhousie UniversityUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineUlcerative colitisHealth professionalsInflammatory bowel diseaseHealth careComputer-assisted web interviewingIrritable bowel syndromeDiseaseFamily medicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Background The Communicating Needs and Features of IBD Experiences (CONFIDE) study aims to increase understanding of the impact of symptoms on patients with moderate to severe UC and Crohn’s disease and to investigate gaps in communication with healthcare professionals (HCPs) in the United States (US), Europe (EUR), and Japan. Purpose This report focuses on patients with moderate to severe UC and HCPs from the US and EUR. Method Online, quantitative, cross-sectional surveys of patients with UC and HCPs were conducted in the US and EUR (France, Germany, Italy, Spain, and UK). HCP surveys included physicians and non-physician HCPs responsible for making prescribing decisions. Moderate to severe UC was defined based on treatment, steroid use, and/or hospitalization history. Data collected included perspectives on the experience of patients with UC. Result(s) A total of 200 US (62% male, mean age 40.4 years) and 556 EUR patients (57% male, mean age 38.9 years), and 200 US and 503 EUR HCPs completed the survey. According to US and EUR patients, the top 3 symptoms currently (past month) experienced were diarrhoea (63% and 50%), bowel urgency (47% and 30%) and increased stool frequency (39% and 30%). Blood in stool was reported as currently experienced by 27% and 24% of US and EUR patients, respectively. Among patients currently experiencing bowel urgency, 47% of US and 27% of EUR patients discuss this symptom at every appointment. Among those who do not discuss bowel urgency at every appointment, 74% and 75% of US and EUR patients would like to discuss this symptom more frequently with their HCP. A total of 30% and 43% of US and EUR patients that ever experienced bowel urgency were not comfortable reporting it to their HCP, with 62% and 58% of these US and EUR patients feeling embarrassed talking about this symptom (Table). HCPs in both the US and EUR ranked diarrhoea (74% and 65%), blood in stool (69% and 65%) and increased stool frequency (38% and 34%) as the top 3 symptoms most reported by patients. According to US and EUR HCPs, the top 4 symptoms proactively discussed in routine appointments were blood in stool (93% and 94%), diarrhoea (90% and 91%), increased stool frequency (82% and 82%) and bowel urgency (76% and 82%). Among HCPs who did not proactively discuss bowel urgency, 47% of US and 40% of EUR HCPs expect patients to bring this up if it is an issue. Image Conclusion(s) Communication gaps were similar between US and EUR patients and HCPs. Bowel urgency is the second-most reported symptom by patients with moderate to severe UC. However, this symptom is not among the HCP-perceived top 3 most reported symptoms. Although a substantial proportion of patients reported a desire to discuss bowel urgency more frequently with their HCP, some patients reported feeling embarrassed talking about it. Many HCPs who do not proactively discuss this symptom expect patients to bring this up. A communication gap was identified and highlights the under-appreciation of bowel urgency as an important symptom of UC. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; Eli Lilly and Company Disclosure of Interest S. Travis Grant / Research support from: AbbVie, BUHLMANN Diagnostics, ECCO, Eli Lilly and Company, Ferring Pharmaceuticals, International Organization for the Study of Inflammatory Bowel Disease, Janssen, Merck Sharp & Dohme, Normal Collision Foundation, Pfizer, Procter & Gamble, Schering-Plough, Takeda, UCB Pharma, Vifor Pharma, and Warner Chilcott, A. Bleakman Employee of: Eli Lilly and Company, D. Rubin Grant / Research support from: Takeda, Consultant of: AbbVie, Allergan, AltruBio, American College of Gastroenterology, Arena Pharmaceuticals, Athos Therapeutics, Bellatrix Pharmaceuticals, Boehringer Ingelheim, Bristol Myers Squibb, Celgene/Syneos Health, Cornerstones Health (non-profit), Eli Lilly and Company, Galen/Atlantica, Genentech/Roche, Gilead Sciences, GoDuRn, InDex Pharmaceuticals, Ironwood Pharmaceuticals, Iterative Scopes, Janssen, Materia Prima, Pfizer, Prometheus Therapeutics and Diagnostics, Reistone Biopharma, Takeda, and TechLab, M. Dubinsky Shareholder of: Trellus Health, Grant / Research support from: AbbVie, Janssen, Pfizer, and Prometheus Biosciences, Consultant of: AbbVie, Arena Pharmaceuticals, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly and Company, F. Hoffmann-La Roche, Genentech, Gilead Sciences, Janssen, Pfizer, Prometheus Therapeutics and Diagnostics, Takeda, and UCB Pharma, R. Panaccione Grant / Research support from: AbbVie, Ferring Pharmaceuticals, Janssen, Pfizer, and Takeda, Consultant of: Abbott, AbbVie, Alimentiv, Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Cosmo Pharmaceuticals, Eisai, Elan Pharma, Eli Lilly and Company, Ferring Pharmaceuticals, Galapagos NV, Genentech, Gilead Sciences, GlaxoSmithKline, Janssen, Merck, Mylan, Oppilan Pharma, Pandion Therapeutics, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz, Satisfai Health, Shire, Sublimity Therapeutics, Takeda, Theravance Biopharma, and UCB Pharma, T. Hibi Grant / Research support from: AbbVie, Activaid, Alfresa Pharma, Bristol Myers Squibb, Eli Lilly Japan K.K., Ferring Pharmaceuticals, Gilead Sciences, Janssen Pharmaceutical K.K., JMDC, Nippon Kayaku, Mochida Pharmaceutical, Pfizer Japan, and Takeda, Consultant of: AbbVie, Apo Plus Station, Bristol Myers Squibb, Celltrion, EA Pharma, Eli Lilly and Company, Gilead Sciences, Janssen, Kyorin, Mitsubishi Tanabe Pharma, Nichi-Iko Pharmaceutical, Pfizer, Takeda, and Zeria Pharmaceutical, Speakers bureau of: AbbVie, Aspen Japan K.K., Ferring Pharmaceuticals, Gilead Sciences, Janssen, JIMRO, Mitsubishi Tanabe Pharma, Mochida Pharmaceutical, Pfizer, and Takeda, T. Gibble Employee of: Eli Lilly and Company, C. Kayhan Employee of: Eli Lilly and Company, E. Flynn Employee of: Eli Lilly and Company, C. Sapin Employee of: Eli Lilly and Company, C. Atkinson Consultant of: Eli Lilly and Company in connection with the development of this publication, Employee of: Adelphi Real World, S. Schreiber Grant / Research support from: personal fees and/or travel support from: AbbVie, Amgen, Arena Pharmaceuticals, Biogen, Bristol Myers Squibb, Celgene, Celltrion, Eli Lilly and Company, Dr. Falk Pharma, Ferring Pharmaceuticals, Fresenius Kabi, Galapagos NV, Gilead Sciences, I-MAB Biopharma, Janssen, Merck Sharp & Dohme, Mylan, Novartis, Pfizer, Protagonist Therapeutics, Provention Bio, Roche, Sandoz/Hexal, Shire, Takeda, Theravance Biopharma, and UCB Pharma, J. Jones: None Declared

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)0,004
score de la tête « metaresearch » (Gemma)0,009
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 enseignants0,007
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,020
Tête enseignante GPT0,308
Écart entre enseignants0,288 · 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

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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