Insights into optimal basal insulin titration in type 2 diabetes: <scp>R</scp> esults of a quantitative survey
Bibliographic record
Abstract
AIMS: Basal insulin (BI) treatment initiation and dose titration in type 2 diabetes (T2DM) are often delayed. Such "clinical inertia" results in poor glycaemic control and high risk of long-term complications. This survey aimed to determine healthcare professional (HCP) and patient attitudes to BI initiation and titration. METHODS: An online survey (July-August 2015) including HCPs and patients with T2DM in the USA, France and Germany. Patients were ≥18 years old and had been on BI for 6 to 36 months, or discontinued BI within the previous 12 months. RESULTS: Participants comprised 386 HCPs and 318 people with T2DM. While >75% of HCPs reported discussing titration at the initiation visit, only 16% to 28% of patients remembered such discussions, many (32%-42%) were unaware of the need to titrate BI, and only 28% to 39% recalled mention of the time needed to reach glycaemic goals. Most HCPs and patients agreed that more effective support tools to assist BI initiation/titration are needed; patients indicated that provision of such tools would increase confidence in self-titration. HCPs identified fear of hypoglycaemia, failure to titrate in the absence of symptoms, and low patient motivation as important titration barriers. In contrast, patients identified weight gain, the perception that titration meant worsening disease, frustration over the time to reach HbA1c goals and fear of hypoglycaemia as major factors. CONCLUSION: A disconnect exists between HCP- and patient-perceived barriers to effective BI titration. To optimize titration, strategies should be targeted to improve HCP-patient communication, and provide support and educational tools.
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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 | 0.010 | 0.022 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".