Global survey investigating causes of treatment inertia in type 2 diabetes cardiorenal risk management
Bibliographic record
Abstract
AIM: To explore reasons behind treatment inertia in current approaches to early cardiorenal risk management in type 2 diabetes (T2D). METHODS: A global, web-based, quantitative panel survey of primary care physicians (PCPs) and primary care diabetes specialists treating people living with T2D. The questions covered current management of T2D, particularly the use of sodium-glucose co-transporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors as second-/third-line therapies. RESULTS: Of 1677 respondents from 18 countries who completed the survey, 73.4% were responsible for second-/third-line therapy initiation. Two thirds had modified treatment decisions based on recent cardiovascular outcomes trials (CVOTs). Respondents cited restricted access to therapies and limits on regular appointments as the greatest barriers to second-/third-line therapy prescription. Although 81.6% agreed that early intensification to second-/third-line therapies is associated with clinical benefits, 46.1% of respondents still reserve these for later lines of therapy, and 23.8% would not consider changing therapeutic approach in patients with well-controlled T2D but increasing cardiovascular risk. CONCLUSIONS: Substantial barriers still prevent optimization of primary setting T2D patient care. Education programs which enable PCPs to translate CVOT evidence into clinical benefits for patients with T2D could address many of the remaining knowledge gaps identified.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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".