512-P: Physician Perspectives and Experiences with Basal Insulin Titration in Type 2 Diabetes: A U.S. Cross-Sectional Survey
Bibliographic record
Abstract
Introduction: Appropriate titration of basal insulin (BI) is essential to achieve glycemic control in patients with type 2 diabetes (T2D) . However, optimal glucose control is often unattained due to challenges with BI titration. This survey study reports physician experiences with BI titration. Methods: Physicians in the Optum Research Database who treated ≥30 patients with T2D, with ≥1 who initiated BI from 10/01/20 to 03/31/21, were asked to complete a one-time mailed survey. Results: Among 386 responders, 70% were male, 76% in general practice, 63% had ≥20-years’ experience, and 29% practiced in rural regions. Most (90%) physicians reported seeing >50 patients with T2D in the prior 6 months, and 66% treated ≥25% of them with BI. The majority (77%) expected glucose control attainment within 12 weeks, and 51% estimated <25% of patients were unable to reach HbA1c goal. Most (86%) physicians explained titration to all/most of their patients new to BI. Only 27% expected patients to self-manage titration; 60% managed titration for them, and 13% did both. Most (84%) physicians had in-office titration education by healthcare providers, 42% provided ongoing access to them; only 13% provided digital tools. The majority expressed concerns about patients’ abilities to follow titration algorithms (79%) , and to monitor blood glucose effectively (66%) , as well as lack of engagement in the titration process (72%) . Conclusion: Physicians reported counseling most new-to-BI patients for titration and providing ongoing clinical guidance and non-digital support tools. Despite this, the majority of physicians expressed concern about patients' abilities to manage self-titration effectively. These findings support the need for novel approaches or tools to support patients during BI titration. Disclosure S.B.Harris: Consultant; Abbott, AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi, Other Relationship; Abbott, AstraZeneca, Bayer Inc., Dexcom, Eli Lilly and Company, HLS Therapeutics, Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Research Support; Applied Therapeutics Inc., AstraZeneca, Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF) , Novo Nordisk, Sanofi, The Lawson Foundation. J.Seufert: Advisory Panel; Abbott, Sanofi-Aventis Deutschland GmbH, Research Support; Boehringer Ingelheim International GmbH, Speaker's Bureau; Abbott Diabetes, AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Lilly, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. M.Bertolini: Employee; Sanofi. V.Walker: Consultant; Sanofi-Aventis U.S. J.C.White: Consultant; Sanofi-Aventis U.S. F.L.Zhou: Employee; Sanofi, Stock/Shareholder; Sanofi. K.Mohammedi: Board Member; Lilly, Novo Nordisk, Sanofi, Research Support; Cyclerion Therapeutics, Inc., Speaker's Bureau; Abbott, AstraZeneca. J.E.Anderson: Advisory Panel; Abbott Diabetes, Consultant; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Gelesis, Novo Nordisk, Sanofi, Speaker's Bureau; Eli Lilly and Company.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".