Patient profile based management approach for Optimal Treatment of Angina: a consensus from India cases
Bibliographic record
Abstract
Chronic stable angina (CSA) is an incapacitating disorder. The pain can hinder the routine chores of an individual and significantly impact one’s quality of life (QoL). However, the good news is that this can be treated and the QoL can be improved. The key to apt management lies in the accurate early diagnosis of this condition, followed by a detailed evaluation and accordingly planned management, which should be regularly revised and be backed by an adequate follow-up. OPTA-OPtimal Treatment for chronic stable Angina-is an educational initiative to assist the clinicians in India with screening and diagnostic tools, strengthened by updated guideline-directed management to ensure satisfactory patient outcomes. OPTA aims to improve clinical outcomes by providing optimized pharmacotherapy for patients with stable angina. This expert consensus document intends to provide information for better understanding of the condition by clinicians and to ensure an early, accurate diagnosis, followed by optimal management of angina. For better clinical and practical understanding of Indian clinical scenario, the most commonly encountered patient profiles are briefly described here. These inputs and an extensive literature review were blended to develop the recommendations for clinicians across the country. An attempt is made to include clinical recommendations that meet the needs of the majority of patients in most circumstances in the Indian scenario. However, the ultimate judgment regarding individual case management should be based on clinician’s discretion. This expert consensus document is not a substitute for textbooks and/or a clinical judgment.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".