Consensus statement on cardiovascular risk stratification and aggressive management of chronic coronary syndromes
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are responsible for 17.5 million deaths globally, and India is more susceptible to coronary artery disease (CAD) than its western counterparts because of certain challenges such as suboptimal management, increased healthcare costs, and physicians undermining the extent of limitation in angina patients. All of these could be resolved by employing strategies starting with accurate diagnosis to timely implementation of aggressive management techniques to contain morbidity and mortality. Pan India, experts from the field of cardiology came together in order to discuss risk stratification and aggressive management of chronic coronary syndromes (CCS). The expert consensus laid down a path of recommendations considering the prevailing healthcare service infrastructure, local evidence-based studies and key international guidelines. With the wide diversity in terms of geographical distribution in India and the risk factors presented across various divisions of the population, risk stratification is necessary for prompt identification and subsequent management of CCS. In this consensus, various risk stratification techniques such as risk stratification based on ventricular function, electrocardiogram (ECG), stress echocardiography (ECHO), the Duke treadmill score (DTS), and myocardial perfusion imaging (MPI) were discussed. After risk stratification, the consensus focused on aggressive management of CVDs with therapeutic optimization for various types of patient profiles such as revascularization for stable angina and angina with comorbidities (diabetes and renal failure), apart from emphasis on various medications and their roles in ameliorating disease symptoms.
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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".