The myocardial and neuronal infectivity of SARS-CoV-2 and detrimental outcomes
Bibliographic record
Abstract
The epidemiological outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), alias COVID-19, began in Wuhan, Hubei, China, in late December and eventually turned into a pandemic that has led to over 3.71 million deaths and over 173 million infected cases worldwide. In addition to respiratory manifestations, COVID-19 patients with neurological and myocardial dysfunctions exhibit a higher risk of in-hospital mortality. The immune function tends to be affected by cardiovascular risk factors and is thus indirectly related to the prognosis of COVID-19 patients. Many neurological symptoms and manifestations have been reported in COVID-19 patients; however, detailed descriptions on the prevalence and characteristic features of these symptoms are restricted due to insufficient data. It is thus advisable for clinicians to be vigilant for both cardiovascular and neurological manifestations to detect them at an early stage to avoid inappropriate management of COVID-19 and to address the manifestations adequately. Patients with severe COVID-19 are notably more susceptible to developing cardiovascular and neurological complications than non-severe COVID-19 patients. This review focuses on the consequential outcomes of COVID-19 on cardiovascular and neuronal functions, including other influencing factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".