Women and Diabetes: Preventing Heart Disease in a New Era of Therapies
Bibliographic record
Abstract
Despite major advances in cardiovascular research over the past decade, women with type 2 diabetes have a high risk of cardiovascular events. Several factors contribute to the poor prognosis for women, including higher levels of frailty and comorbidities, but their cardiovascular risk is underestimated and there is suboptimal implementation and uptitration of new evidence-based therapies, leading to high morbidity and mortality. Recent studies highlight the need for better management of diabetes in women that can be pursued and achieved in light of recent results from randomised controlled trials demonstrating evidence of the benefits of new therapeutic strategies in improving cardiovascular outcomes and quality of life of women covering the entire cardiovascular continuum. This review critically discusses the multiple benefits for women of new pharmacological treatments, such as glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter type 2 inhibitors (SGLT2i), proprotein convertase subtilisin/kexin type 9 inhibitors, inclisiran, icosapent ethyl and bempedoic acid in preventing cardiovascular events, and treatments, such as angiotensin receptor neprilysin inhibitors, SGLT2i, vericiguat and omecamtiv mecarbil, for preventing heart failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".