Cardiac impairments in <scp>postacute COVID</scp> ‐19 with sustained symptoms: A review of the literature and proof of concept
Bibliographic record
Abstract
Although acute COVID-19 is known to cause cardiac damage in some cases, there is still much to learn about the duration and relative permanence of the damage that may occur. Long COVID is a condition that can occur when COVID-19 symptoms remain in the postviral acute period. Varying accounts of long COVID have been described across the literature, however, cardiac impairments are sustained in many individuals and cardiovascular assessment is now considered to be an expected follow-up examination. The purpose of this review and proof of concept is to summarize the current research related to the assessment of cardiac function, including echocardiography and blood biomarker data, during the follow-up period in patients who recovered from COVID-19. Following a literature review, it was found that right ventricular dysfunction along with global longitudinal strain and diastolic dysfunction are common findings. Finally, more severe acute myocardial injury during the index hospitalization appears to exacerbate cardiac function. The available literature implies that cardiac function must be monitored in patients recovered from COVID-19 who remain symptomatic and that the impairments and severity vary from person-to-person. The proof-of-concept analysis of patients with cardiac disease and respiratory disease in comparison to those with sustained symptoms from COVID-19 suggests elevated systolic time interval in those with sustained symptoms from COVID-19, thus reducing heart performance indices. Future research must consider the details of cardiac complications during the acute infection period and relate this to the cardiac function in patients with long COVID during mid- and long-term follow-up.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".