Abstract 15244: Synchronous Care in Cardiovascular Disease: The DECIDE-CV Clinic Program
Bibliographic record
Abstract
Background: The care model for type 2 diabetes (T2D) and its main complications is thought to be “asynchronous” and associated with delays in care and low use of guideline-directed medical therapies (GDMT). Hypothesis: A synchronous care clinic for T2D patients, including a simultaneous evaluation by cardiology/endocrinology and nephrology, will enhance the rate of GDMT use and improve biomarkers of interest. Methods: Retrospectively analysis from patients evaluated in the DECIDE-CV clinic, a cardiometabolic clinic for T2D patients with either atherosclerotic cardiovascular disease, heart fialure (HF) or chronic kidney disease (CKD). Baseline data was compared to the data obtained in the last available visit. For patients with only one visit, the treatment prescribed at the end of the first visit was considered in the last visit group. Categorical data is presented as frequencies (proportions) and was analyzed using McNemar test. Continuous data is presented as mean±SD or median (IQR) as appropriate and was compared using the t-test for paired data or Wilcoxon test. Results: 150 patients, 72% male with a mean age of 67±12 years. 115 (78%) had atherosclerotic cardiovascular disease, 98 (65%) had HF and 77 (51%) CKD. Comparing baseline to last visit data, there was a statistically significant increase in GDMT and de-escalation of insulin, sulfonylureas and DPP4i (Table). For patients with two sets of available laboratory data, there was a significant decrease in N-terminal pro B-type natriuretic peptide (504 [155-1160] pg/mL vs 335 [150-1031] pg/mL, p=0.03) and albuminuria (57 [19-259] mg/g vs 57 [9-155] mg/g, p<0.01). Conclusion: A synchronous care clinic for T2D patients is associated with increased use of GDMT, de-escalation of therapy that lack morbidity/mortality benefit, and improvement in prognostic biomarkers. These results can potentially translate into better outcomes in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".