Positive and Negative Affect Is Related to Experiencing Chest Pain During Exercise-Induced Myocardial Ischemia
Bibliographic record
Abstract
OBJECTIVE: Silent myocardial ischemia is thought to be associated with worse cardiovascular outcomes due to a lack of perception of pain cues that initiate treatment seeking. Negative affect (NA) has been associated with increased pain reporting and positive affect (PA) with decreased pain reporting, but these psychological factors have not been examined within the context of myocardial ischemia. This study evaluated the associations between PA, NA, and chest pain reporting in patients with and without ischemia during exercise testing. METHODS: A total of 246 patients referred for myocardial perfusion single-photon emission computed tomography exercise stress testing completed the positive and negative affect schedule-expanded version, a measure of PA and NA. Presence of chest pain and myocardial ischemia were evaluated using standardized protocols. RESULTS: Logistic regression analyses revealed that for every 1-point increase in NA, there was a 13% higher chance for ischemic patients (odds ratio [OR] = 1.13; 95% confidence interval [CI] = 1.02 to 1.26) and an 11% higher chance in nonischemic patients (OR = 1.11; 95% CI = 1.03 to 1.19) to report chest pain. A significant interaction of PA and NA on chest pain reporting (β = 0.02; 95% CI = 0.002 to 0.031) was also observed; nonischemic patients with high NA and PA reported more chest pain (57%) versus patients with low NA and low PA (13%), with high NA and low PA (17%), and with high PA and low NA (7%). CONCLUSIONS: Patients who experience higher NA are more likely to report experiencing chest pain. In patients without ischemia, high NA and PA was also associated with a higher likelihood of reporting chest pain. Results suggest that high levels of PA as well as NA may increase the experience and/or reporting of chest pain.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".