Relationship between Weight Change Patterns and Health Satisfaction in the CANadian Canagliflozin Registry (CanCARE) Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".