Association of coronary ischemia estimated by fractional flow reserve and psychological characteristics of patients
Bibliographic record
Abstract
INTRODUCTION: Psychological characteristics of patients, depression, stress and anxiety are recognized as important confounding risk factors for ischemic heart disease. However, the impact of psychological characteristics on coronary ischemia and vice versa remain poorly understood. AIM: To demonstrate the interplay of psychological characteristics, depression, stress and anxiety with coronary ischemia estimated with fractional flow reserve (FFR). MATERIAL AND METHODS: From 2014 to 2016, 147 patients who were planned for FFR measurement were included in this study. Psychological characteristics of patients were evaluated using the Depression, Anxiety and Stress Scale 21 items (DASS 21) self-report questionnaire. RESULTS: Comparing the FFR ischemic vs. FFR non-ischemic groups, a significant difference was observed regarding results achieved for the depression, anxiety and stress scales. Multivariate logistic regression analysis was used to model the correlation between FFR and the DAS scale. It was clear, when controlling for previous myocardial infarction, that FFR was significant in all analyses. However, when the Canadian Cardiovascular Society grading of angina pectoris (CCS) class was entered in the model, FFR was not a significant predictor of anxiety, but was significant in other analysis. CONCLUSIONS: Higher degrees of the psychological characteristics depression, stress and anxiety were observed in the group of patients with coronary ischemia, corresponding to lower fractional flow values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".