Nicorandil Versus Nitroglycerin for Symptomatic Relief of Angina in Patients With Slow Coronary Flow Phenomenon
Bibliographic record
Abstract
OBJECTIVE: Patients with the coronary slow flow phenomenon frequently experience angina episodes. The present study aimed to compare the efficacy of nicorandil versus nitroglycerin for alleviation of angina symptoms in slow flow patients. METHODS: In a single-center, single-blind, parallel-design, comparator-controlled, randomized clinical trial (NCT02254252), 54 patients with slow flow and normal or near-normal coronary angiography who presented with frequent angina episodes were randomly assigned to 1-month treatment with nicorandil 10 mg, 2 times a day (n = 27) or sustained-release glyceryltrinitrate 6.4 mg 2 times a day (n =27). Frequency of angina episodes, pain intensity, and the Canadian Cardiovascular Society (CCS) grading of angina pectoris were assessed at baseline and after 1 month of treatment. RESULTS: In all, 25 patients in the nicorandil arm and 24 patients in the nitroglycerin arm were analyzed. After 1 month, patients treated with nicorandil had fewer angina episodes (adjusted mean number of episodes per week, nicorandil versus nitroglycerin; 1.68 ± 0.15 vs 2.29 ± 0.15, P = .007, effect size = 14.6%). Patients also reported greater reductions in pain intensity with nicorandil versus nitroglycerin (adjusted mean of self-reported pain score; 3.03 ± 0.29 vs 3.89 ± 0.30, P = .046, effect size = 8.4%). A significantly higher proportion of patients in the nicorandil arm were categorized in CCS class I (76% vs 33.3%, P = .004) or class II (16.0% vs 45.8%, P = .032). CONCLUSION: In slow flow patients, nicorandil provides better symptomatic relief of angina than nitroglycerin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".