Patient and physician perspectives and experiences of basal insulin titration in type 2 diabetes in the United States: Cross‐sectional surveys
Bibliographic record
Abstract
AIM: Patient- and physician-associated barriers impact the effectiveness of basal insulin (BI) titration in the management of type 2 diabetes (T2D). We evaluated the experiences of patients with T2D and physicians with BI titration education. MATERIALS AND METHODS: In this observational, cross-sectional study, patients with T2D and physicians treating patients with T2D were identified by claims in the Optum Research Database and were invited to complete a survey. Eligible patients had 12 months of continuous health-plan enrolment with medical and pharmacy benefits during the baseline period, and recent initiation of BI therapy. Eligible physicians had initiated BI for ≥1 eligible patient with T2D during the past 6 months. RESULTS: In total, 416 patients and 386 physicians completed the survey. Ninety per cent of physicians reported treating ≥50 patients with T2D; 66% treated ≥25% of patients with BI. Whereas 74% of patients reported that BI titration was explained to them by a physician, 96% of physicians reported doing so. Furthermore, 20% of patients stated they were offered educational materials whereas 56% of physicians reported having provided materials. Physicians had higher expectations of glycaemic target achievement than were seen in the patient survey; their main concern was the patients' ability to titrate accurately (79%). CONCLUSIONS: There is a marked difference in patients' and physicians' experiences of BI titration education. Novel tools and strategies are required to enable effective BI titration, with more educational resources at the outset, and ongoing access to tools that provide clear, simple direction for self-titration with less reliance on physicians/health care providers.
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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 | 0.009 |
| 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) | 0.001 | 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".