Discussion on Key Heart Failure Risk Factors in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Henry Punzi, MD, from Trinity Hypertension & Metabolic Research Institute, Carrollton, TX; and the Department of Family Medicine UT Southwestern Medical Center Dallas, Texas; moderated the topic Discussion on Key Heart Failure Risk Factors in Patients With Type 2 Diabetes Mellitus with Peter A. McCullough, MD, MPH, from the Department of Internal Medicine at Baylor University Medical Center, Dallas, TX; and Subodh Verma, MD, from the Department of Cardiac Surgery, University of Toronto, Ontario, Canada, The discussion focused primarily on: 1. The link between type 2 diabetes mellitus and heart failure; 2. how heart failure affects patients with type 2 diabetes mellitus; 3. available heart failure risk prediction tools; 4. how to identify patients with type 2 diabetes mellitus at risk for heart failure; 5. screening for heart failure risks in patients with type 2 diabetes mellitus; and 6. the role of dapagliflozin in decreasing the risk for heart failure in patients with type 2 diabetes mellitus. [Published online ahead of print 2020 October (Med Roundtable Cardiovasc Ed. 2020 October.) ©2020 FoxP2Media, LLC This roundtable was supported by AstraZeneca. The discussants (authors) developed the discussion and reviewed the transcript for important intellectual content and approved the final version for publication. The authors maintained control of the discussion and the resulting content of this article.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".