Factors Influencing Efficacy of Nitrate Therapy for Stable Angina Pectoris: A Multiple Linear Regression Analysis
Bibliographic record
Abstract
In an open-label self-controlled study of 1,350 patients with stable angina pectoris (SAP), we previously demonstrated that 50 mg of isosorbide mononitrate (ISMN) slow release formulation once daily not only provided a better antianginal effect but also a better quality of life (QOL) than did the daily administration of multiple small doses of the compound. It is unknown whether certain patient characteristics contribute to this benefit. The objective of this article was to determine what independent factors contribute to this benefit. Multiple linear regression analysis was performed on the data from these 1,350 patients. Quality of life was assessed by the Marquis QOL-questionnaire for patients with angina and included the domains of immobility, pain, and psychological distress. For the purpose of this study, overall QOL was calculated as the pooled sums of the domain scores and expressed as mean scores on an ordinal scale of 10. Age did not influence the beneficial effect of nitrate therapy on QOL. Neither did gender, rhythmic disturbances, peripheral artery disease, or the concomitant use of calcium channel blockers or beta blockers. New York Heart Association (NYHA) angina classification was an independent variable: patients with a NYHA class I or II benefited less than did patients with NYHA III or IV (p = 0.02). Obese patients as well as hypertensive patients benefited less (p = 0.04 and 0.02), and smokers tended to benefit less also (p = 0.08). In contrast, hypercholesterolemia and diabetes mellitus improved the beneficial effect of nitrates on QOL (p = 0.03 and 0.05). The authors conclude that patients with coronary artery disease (CAD) and concomitant diabetes mellitus or hypercholesterolemia, a category particularly prone to early endothelial dysfunction and thus dysfunctional endogenous nitric oxide (NO) production, may benefit more from NO-donor therapy than patients without such concomitant conditions.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".