Pharmacological management of patients with ANOCA in Bulgaria: insights from a single-center registry
Bibliographic record
Abstract
Introduction: Angina with non-obstructive coronary arteries (ANOCA) is frequently encountered in clinical practice but remains poorly understood and inconsistently managed. Despite the absence of signifi cant coronary stenoses, patients often report persistent symptoms and receive extensive pharmacotherapy. Real-world data on medication use and symptom burden in ANOCA populations remain limited. Material and methods: We conducted a single-center observational study of 102 patients referred to coronary angiography due to angina, who were subsequently found to have non-obstructive coronary artery disease. Baseline medication use and symptom severity were assessed using the Canadian Cardiovascular Society (CCS) classifi cation and Seattle Angina Questionnaire (SAQ). Associations between treatment and symptoms were analyzed using non-parametric tests. Results: The mean age was 61 years; 59% were women. Over 90% of patients were on cardiovascular medications, with 22.6% receiving fi ve or more agents. The most used therapies were β-blockers (59.8%), ACE inhibitors/ARBs (70.6%), and statins (58.8%). Despite this, 54.4% were in CCS class II or higher, and SAQ scores refl ected persistent symptoms. No signifi cant associations were found between drug class or medication count and symptom severity. Trimetazidine use was associated with slightly higher CCS class (p = 0.032). Conclusion: In this ANOCA cohort, pharmacotherapy was intensive but not clearly associated with symptom control. These fi ndings highlight the need for individualized, endotype-guided treatment strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".