Utilizing synchronous care to improve cardiovascular and renal health among patients with type 2 diabetes: Proof‐of‐concept results from the <scp>DECIDE</scp> ‐ <scp>CV</scp> clinical programme
Bibliographic record
Abstract
AIM: The management of patients with type 2 diabetes is asynchronous, i.e. not coordinated in time, resulting in delayed access to care and low use of guideline-directed medical therapy (GDMT). METHODS: We retrospectively analysed consecutive patients assessed in the 'synchronized' DECIDE-CV clinic. In this outpatient clinic, patients with type 2 diabetes and cardiovascular or chronic kidney disease are simultaneously assessed by an endocrinologist, cardiologist and nephrologist in the same visit. The primary outcome was use of GDMT before and after the assessment in the clinic, including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, renin-angiotensin system blockers and mineralocorticoid receptor antagonists. Secondary outcomes included the baseline-to-last-visit change in surrogate laboratory biomarkers. RESULTS: The first 232 patients evaluated in the clinic were included. The mean age was 67 ± 12 years, 69% were men and 92% had diabetes. In total, 73% of patients had atherosclerotic cardiovascular disease, 65% heart failure, 56% chronic kidney disease and 59% had a urinary albumin-to-creatinine ratio ≥30 mg/g. There was a significant increase in the use of GDMT:sodium-glucose cotransporter 2 inhibitors (from 44% to 87% of patients), glucagon-like peptide 1 receptor agonists (from 8% to 45%), renin-angiotensin system blockers (from 77% to 91%) and mineralocorticoid receptor antagonists (from 25% to 45%) (p < .01 for all). Among patients with paired laboratory data, glycated haemoglobin, urinary albumin-to-creatinine ratio and N-terminal proB-type natriuretic peptide levels significantly dropped from baseline (p < .05 for all). CONCLUSIONS: Joint assessment of patients with diabetes in a synchronized cardiometabolic clinic holds promise for enhancing GDMT use and has led to significant reductions in surrogate cardiovascular and renal laboratory biomarkers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".