The Effect of Percutaneous Coronary Intervention on QT Dispersion and the Association Between Them: A Systematic Review
Bibliographic record
Abstract
Electrocardiography (ECG) parameters are significant in the prognosis of ischemia and other cardiovascular conditions. Reperfusion or revascularization techniques are essential in reestablishing blood flow to ischemic tissues. This study aims to demonstrate the association between percutaneous coronary intervention (PCI), a revascularization technique, and the electrocardiography (ECG) parameter, QT dispersion (QTd). We conducted a systematic review of the association between PCI and QTd through a literature search in three electronic databases, ScienceDirect, PubMed, and Google Scholar, for empirical studies published in English. Review Manager (RevMan) 5.4 (Cochrane Collaboration, Oxford, England) was used for statistical analysis. Of 3,626 studies, 12 articles met the inclusion criteria, enrolling a total of 1,239 patients. After a successful PCI procedure, QTd and corrected QT (QTc) tremendously reduced at various time intervals with statistical significance in most of the studies. There was a clear association between ECG parameters QTd, QTc, and corrected QT dispersion (QTcd), and PCI, in that there is a considerable reduction in these ECG parameters after PCI treatment.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".