Heart Disease and Left Ventricular Rotation – A Systematic Review and Quantitative Summary
Bibliographic record
Abstract
BACKGROUND: Left ventricular (LV) rotation is increasingly examined in those with heart disease. The available evidence measuring LV rotation in those with heart diseases has not been systematically reviewed. METHODS: To review systematically the evidence measuring LV rotational changes in various heart diseases compared to healthy controls, literature searches were conducted for appropriate articles using several electronic databases (e.g., MEDLINE, EMBASE). All randomized-controlled trials, prospective cohort and case-controlled studies that assessed LV rotation in relation to various heart conditions were included. Three independent reviewers evaluated each investigation's quality using validated scales. Results were tabulated and levels of evidence assigned. RESULTS: A total of 1,782 studies were found through the systematic literature search. Upon review of the articles, 47 were included. The articles were separated into those investigating changes in LV rotation in participants with: aortic stenosis, myocardial infarction, hypertrophic cardiomyopathy, dilated cardiomyopathy, non-compaction, restrictive cardiomyopathy/ constrictive pericarditis, heart failure, diastolic dysfunction, heart transplant, implanted pacemaker, coronary artery disease and cardiovascular disease risk factors. Evidence showing changes in LV rotation due to various types of heart disease was supported by evidence with limited to moderate methodological quality. CONCLUSIONS: Despite a relatively low quality and volume of evidence, the literature consistently shows that heart disease leads to marked changes in LV rotation, while rotational systolic-diastolic coupling is preserved. No prognostic information exists on the potential value of rotational measures of LV function. The literature suggests that measures of LV rotation may aid in diagnosing subclinical aortic stenosis and diastolic dysfunction.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".