Management of Hypertension and Associated Comorbidities: An Expert Consensus Statement from India
Bibliographic record
Abstract
Hypertension is a leading global health concern that significantly contributes to cardiovascular (CV) and renal diseases. In India, its prevalence is rising, often coexisting with comorbidities such as type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), coronary artery disease (CAD), and metabolic syndrome (MetS). Effective hypertension management in these populations is challenging due to variations in blood pressure (BP) targets, the need for combination therapy, and the complexity of treating associated conditions such as albuminuria, nephropathy, CKD, CAD, and acute coronary syndromes (ACS). Despite advancements in treatment options, inconsistencies in clinical practice highlight the need for standardized, evidence-based recommendations. This expert consensus aims to address these gaps by guiding BP targets, optimal antihypertensive strategies, and individualized treatment approaches for high-risk patients. Key considerations include the role of renin-angiotensin-aldosterone system blockers, calcium channel blockers (CCBs), beta-blockers (BBs), sodium-glucose cotransporter-2 inhibitors, and combination therapies in improving CV and renal outcomes. By establishing clear, consensus-driven recommendations, this statement seeks to enhance hypertension management, promote early intervention, and improve patient outcomes in India.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".