Postmortem Findings Associated With SARS-CoV-2
Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19), caused by the novel Severe Acute Respiratory Syndrome-associated Coronavirus 2 (SARS-CoV-2), has become a global threat to public health. COVID-19 is more pathogenic and infectious than the prior 2002 pandemic caused by SARS-CoV-1. The pathogenesis of certain disease manifestations in COVID-19 such as diffuse alveolar damage (DAD) are thought to be similar to SARS-CoV-1. However, the exact pathogenesis of COVID-19 related deaths remains poorly understood. The aim of this article was to systematically summarize the rapidly emerging literature regarding COVID-19 autopsies. A meta-analysis was also conducted based on data accrued from preprint and published articles on COVID-19 (n=241 patients) and the results compared with postmortem findings associated with SARS-CoV-1 deaths (n=91 patients). Both autopsy groups included mostly adults of median age 70 years with COVID-19 and 50 years with SARS-CoV-1. Overall, prevalence of DAD was more common in SARS-CoV-1 (100.0%) than COVID-19 (80.9%) autopsies (P=0.001). Extrapulmonary findings among both groups were not statistically significant except for hepatic necrosis (P <0.001), splenic necrosis (P<0.006) and white pulp depletion (P <0.001) that were more common with SARS-CoV-1. Remarkable postmortem findings in association with COVID-19 apart from DAD include pulmonary hemorrhage, viral cytopathic effect within pneumocytes, thromboembolism, brain infarction, endotheliitis, acute renal tubular damage, white pulp depletion of the spleen, cardiac myocyte necrosis, megakaryocyte recruitment, and hemophagocytosis.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".