Clinical Research Nonischemic Chest Pain Following Successful Percutaneous Coronary Intervention at a Regional Referral Centre in Southern Ontario
Bibliographic record
Abstract
Background: The purpose of this study was to identify factors that predispose individuals to nonischemic chest pain following successful percutaneous coronary intervention (PCI). Methods: We prospectively followed, for 6 months, a cohort of 110 patients who underwent PCI. We determined baseline factors associated with post PCI pain via nonlinear mixed model regression; a binomial distribution with logit link was used. Results: The mean age of participants (n 110) was 64 (SD 11.19), 69% were male. The majority had 1 coronary vessel dilated (88%) and a single stent placement (67%). During follow-up, chest pain was prevalent in 54% (95% confidence interval [CI], 44.8-63.7) and 45% (95% CI, 36.0-54.8) of patients, at 3 and 6 months respectively. Less than half of those with chest pain were evaluated for ischemia. Of those evaluated, tests were negative for the majority; 74% and 61% at 3 and 6 months respectively. Higher baseline depression (odds ratio 1.50; 95% CI, 1.13-1.99) scores (Hospital Anxiety Depression Scale) were significantly associated with nonischemic chest pain during follow-up. Conclusion: Higher baseline depression scores were found to be significant risk factors for chest pain of nonischemic origin following successful PCI. A larger study is needed to confirm the predictive
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".