Relationship between Weight Change Patterns and Health Satisfaction in the CANadian Canagliflozin Registry (CanCARE) Study
Notice bibliographique
Résumé
Patient health satisfaction is associated with positive health behaviors and is considered important for optimal management of type 2 diabetes mellitus (T2DM). In previously reported randomized clinical trials (RCTs) of canagliflozin (CANA), CANA reduced HbA1c, body weight, and blood pressure. CanCARE is a prospective, observational, 12-month registry for people living with T2DM newly initiated on CANA. The Current Health Satisfaction Questionnaire (CHES-Q) was administered in this study at baseline (BL) and repeated at 3, 6 and 12 months. We report the pre-specified analyses investigating the relationship between current health satisfaction agreement on CHES-Q with weight change patterns defined as: Pattern 1 (loss from BL to month 3 and loss from month 3 to 12); Pattern 2 (loss from BL to month 3 and gain from month 3 to 12); Pattern 3 (gain from BL to month 3 and loss from month 3 to 12); and Pattern 4 (gain from BL to month 12). At month 12, 75.4% of subjects (389/516) completed the CHES-Q, of which 318 patients, with available data, had a mean weight loss of 3.2 kg (7.1 lbs). Proportion of subjects with weight loss patterns were: 55.5%, 25.5%, 13.2% and 4.8% for patterns 1, 2, 3, and 4, respectively. Satisfaction agreement with current health increased from 46.5% to 67.9%, 52.5% to 63.9%, 40.0% to 44.7%, and 20.0% to 26.7% within weight loss patterns 1, 2, 3, and 4, respectively. Based on 290 subjects with available data, 64.2%, 65.3% and 60.5% in patterns 1, 2, and 3 reported satisfaction agreement maintenance or improvement with current health between baseline and month 12; with relatively less maintenance or improvement (46.7%) among those with Pattern 4 (weight gain). This analysis shows that real world use of CANA offers weight loss consistent with that seen in CANA RCTs and suggests a direct correlation between weight change patterns and health satisfaction for people living with T2DM. Disclosure V.C. Woo: Advisory Panel; Self; Janssen Pharmaceuticals, Inc., AstraZeneca, Novo Nordisk Inc., Sanofi, Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc. H.S. Bajaj: Speaker's Bureau; Self; Abbott. Advisory Panel; Self; Amgen Inc.. Speaker's Bureau; Self; Amgen Inc.. Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. Speaker's Bureau; Self; AstraZeneca, Bayer AG. Advisory Panel; Self; Boehringer Ingelheim GmbH. Research Support; Self; Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Janssen Pharmaceuticals, Inc.. Research Support; Self; Janssen Pharmaceuticals, Inc.. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc., Medtronic. Advisory Panel; Self; Merck & Co., Inc.. Research Support; Self; Merck & Co., Inc.. Speaker's Bureau; Self; Merck & Co., Inc., Mylan. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Novo Nordisk Inc.. Advisory Panel; Self; Sanofi. Research Support; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Valeant Pharmaceuticals International, Inc.. Research Support; Self; Valeant Pharmaceuticals International, Inc.. Speaker's Bureau; Self; Valeant Pharmaceuticals International, Inc. M.A. Clement: Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc., AstraZeneca, Novo Nordisk Inc., Sanofi-Aventis, Eli Lilly and Company. F. Camacho: Consultant; Self; Janssen Inc. S. Traina: Employee; Self; Janssen Global Services, LLC. N. Georgijev: Employee; Self; Janssen Scientific Affairs, LLC. J.B. Rose: Employee; Self; Janssen Pharmaceuticals, Inc. D. Sorabji: Employee; Self; Janssen Pharmaceuticals, Inc. A.D. Bell: Advisory Panel; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Bayer AG, Eli Lilly and Company, Novo Nordisk Inc.. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Servier.
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
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 ».