Long COVID Syndrome and Cardiovascular Manifestations: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
(1) Background: Long COVID syndrome is a significant cause of morbidity in COVID-19 patients who remain symptomatic with varied clinical presentations beyond three weeks. Furthermore, the relevance of considering cardiovascular outcomes in post-COVID-19 syndrome is important in the current COVID-19 pandemic; (2) Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for this systematic review and meta-analysis. Systematic searches were conducted from multiple databases without language restrictions until October 8, 2022, to find studies evaluating cardiovascular outcomes such as arrhythmias, myocardium and pericardium diseases, coronary vessel disease, and thromboembolic disorders in post-COVID cases. The pooled odds ratio (OR), and standard mean difference (SMD) with their corresponding 95% confidence intervals (CI) were computed to find the association; (3) Results: Altogether, seven studies with a total of 8,126,462 (cases: 1,321,305; controls: 6,805,157) participants were included in the meta-analysis. Pooled odds ratios of cardiovascular outcomes were significantly higher in post-COVID cases (OR > 1, p < 0.05) than in controls. However, the mortality (OR: 4.76, p = 0.13), and heart rate variability (SMD: −0.06, p = 0.91) between cases and controls were not statistically significant; (4) Conclusions: Significant cardiovascular sequelae in long COVID syndrome highlight the importance of careful cardiac monitoring of COVID-19 patients in the post-COVID phase to address cardiovascular complications as soon as possible; larger-scale prospective studies are required for accurate estimation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it