1188-P: Approaches to and Inertia in Cardiovascular and Renal Risk Management of Type 2 Diabetes Patients in Primary Care: Results from a Global Quantitative Survey
Bibliographic record
Abstract
Background: Despite recent recommendations for the use of glucose-lowering therapies with additional cardiovascular (CV) and renal benefits, as demonstrated through CV outcomes trials (CVOTs), many patients (pts) with type 2 diabetes mellitus (T2D) managed in primary care remain on therapeutic regimens now considered suboptimal. This global survey of primary care physicians (PCPs) investigated current approaches to early CV and renal risk management of pts with T2D. Methods: This web-based, global, quantitative panel survey of PCPs and primary care diabetes specialists (PCDSs) constituted 20 demographic, Likert scale, and multiple-choice questions covering the current management of pts with T2D, particularly the use of second/third line therapies (sodium-glucose co-transporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors). Results: Globally, 1558 PCPs and 119 PCDSs from 18 countries completed the survey. Respondents saw a mean 132 pts with T2D per month and had treated pts with T2D for 17 years. Overall, 73% of PCPs and 72% of PCDSs were responsible for second/third line treatment initiation. The biggest barrier to second/third line treatment initiation identified by respondents was local healthcare systems/insurance companies restricting access to these therapies. Concerns over safety, pt disinterest, and difficulties in maintaining regular appointments were also identified as significant contributors to treatment inertia. Only 68% of PCPs and 52% of PCDSs were aware of data published from recent CVOTs, while 68% and 58%, respectively, stated they had changed their treatment decisions based on these trials. Conclusions: Significant barriers still prevent optimization of T2D pt care in the primary setting, including limited familiarity of PCPs and PCDSs with new CVOT data, treatment inertia, and access restrictions for second/third line treatments. Disclosure S. Brunton: Advisory Panel; Self; Abbott, Bayer U.S., Merck & Co., Inc., Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Lilly Diabetes. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk Inc. F. Cos: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk A/S, Sanofi. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk A/S, Sanofi. G.F. Deed: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk Inc., Sanofi-Aventis. N. Kanumilli: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Janssen Pharmaceuticals, Inc., Novartis AG, Novo Nordisk A/S. Consultant; Self; Boehringer Ingelheim International GmbH, Sanofi-Aventis. Speaker’s Bureau; Self; Napp Pharmaceuticals. P.R. Kushner: Advisory Panel; Self; Abbott, AstraZeneca, Bayer U.S., Janssen Pharmaceuticals, Inc., Novo Nordisk Inc. Speaker’s Bureau; Self; AstraZeneca, Janssen Pharmaceuticals, Inc. P.J. Lin: Speaker’s Bureau; Self; Abbott, AstraZeneca, Bausch + Lomb, Bayer Inc., Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Mead Johnson Nutrition, Merck & Co., Inc., Novo Nordisk Inc., Sun Pharmaceutical Industries Ltd. Other Relationship; Self; Elsevier. J. Nolte: Advisory Panel; Self; AstraZeneca. Funding AstraZeneca
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".