Total cosine R‐to‐T for predicting ventricular arrhythmic and mortality outcomes: A systematic review and meta‐analysis
Bibliographic record
Abstract
Background The total cosine R‐to‐T (TCRT), a vectorcardiographic marker reflecting the spatial difference between the depolarization and repolarization wavefronts, has been used to predict ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD) in different clinical settings. However, its prognostic value has been controversial. Objective This systematic review and meta‐analysis evaluated the significance of TRCT in predicting arrhythmic and/or mortality endpoints. Methods PubMed and Embase databases were searched through December 31, 2016. Results Of the 890 studies identified initially, 13 observational studies were included in our meta‐analysis. A total of 11,528 patients, mean age 47 years old, 72% male, were followed for 43 ± 6 months. Data from five studies demonstrated lower TCRT values in myocardial infarction patients with adverse events (syncope, ventricular arrhythmias, or sudden cardiac death) compared to those without these events (mean difference = −0.36 ± 0.05, p < .001; I2 = 48%). By contrast, only two studies analyzed outcomes in heart failure, and pooled meta‐analysis did not demonstrate significant difference in TCRT between event‐positive and event‐negative patients (mean difference = −0.01 ± 0.10, p > .05; I2 = 80%). Conclusion TCRT is lower in MI patients at high risk of adverse events when compared to those free from such events. It can provide additional risk stratification beyond the use of clinical parameters and traditional electrocardiogram markers. Its value in other diseases such as heart failure requires further studies.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".