CVOT Summit 2022 Report: new cardiovascular, kidney, and glycemic outcomes
Bibliographic record
Abstract
The 8th Cardiovascular Outcome Trial (CVOT) Summit on Cardiovascular, Kidney, and Glycemic Outcomes was held virtually on November 10-12, 2022. Following the tradition of previous summits, this reference congress served as a platform for in-depth discussion and exchange on recently completed outcomes trials as well as key trials important to the cardiovascular (CV) field. This year's focus was on the results of the DELIVER, EMPA-KIDNEY and SURMOUNT-1 trials and their implications for the treatment of heart failure (HF) and chronic kidney disease (CKD) with sodium-glucose cotransporter-2 (SGLT2) inhibitors and obesity with glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonists. A broad audience of primary care physicians, diabetologists, endocrinologists, cardiologists, and nephrologists participated online in discussions on new consensus recommendations and guideline updates on type 2 diabetes (T2D) and CKD management, overcoming clinical inertia, glycemic markers, continuous glucose monitoring (CGM), novel insulin preparations, combination therapy, and reclassification of T2D. The impact of cardiovascular outcomes on the design of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) trials, as well as the impact of real-world evidence (RWE) studies on the confirmation of CVOT outcomes and clinical trial design, were also intensively discussed. The 9th Cardiovascular Outcome Trial Summit will be held virtually on November 23-24, 2023 ( http://www.cvot.org ).
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.036 | 0.019 |
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".