Incidence of left ventricular thrombus following STEMI in the modern era via multimodality imaging: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: -analysis to better understand the incidence and diagnosis of LVT in patients with STEMI treated with timely percutaneous techniques as assessed by multimodality imaging. Methods: Cochrane, EMBASE, LILACS, and MEDLINE were searched over the last 10 years only including studies using contemporary techniques. The primary outcome was detection of LVT in patients via echocardiogram with or without contrast or Cardiac MRI (cMRI) following STEMI (both anterior and any territory) treated with PCI. Data was pooled across studies and statistical analysis was conducted via random effects model. Results: 31 studies were included. 18 studies included data on any territory STEMI, totaling 14,172 patients, and an incidence of 5.6% [95% CI 4.3-7.0]. 18 studies were included in analysis for anterior STEMI, totaling 7382 patients and incidence of 12.7% [95% CI 9.8-15.6]. Relative to cMRI as a gold standard, the sensitivity of non-contrast echocardiography to detect LVT was 58.2% [95% CI 46.6-69.2] with a specificity of 97.8% [95% CI 96.3-98.8]. Conclusions: Incidence of LVT in STEMI patients treated with contemporary timely percutaneous revascularization is in keeping with historical data and remains significant, suggesting this remains an ongoing issue for further investigation. Numerically, both cMRI and contrast echo detected more LVT compared to non-contrast echo in any-territory STEMI patients.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".