Detailed neuropathologic report of COVID-19 complicated by large intracerebral hemorrhage and periventricular lesions with macrophagic infiltrates
Bibliographic record
Abstract
Infection with the SARS-CoV-2 virus affects a wide range of systems. Significant involvement of the central nervous system has been described, including ischemic and hemorrhagic strokes. Thus far, neuropathologic reports of patients who passed away from COVID-19 have generally described non-specific findings, such as variable reactive gliosis and meningeal chronic inflammatory infiltrates, as well as the consequences of the infection's systemic complications on the brain, including ischemic infarcts and hypoxic/ischemic encephalopathy. The neuropathological changes in patients with COVID-19 and large hemorrhagic strokes have not been described in detail. We report the case of an elderly male who had a long course of COVID-19 and ultimately passed away from a large intracerebral hemorrhage. In addition to acute hemorrhage, neuropathologic examination demonstrated non-specific reactive changes and chronic periventricular lesions with macrophagic and perivascular lymphocytic infiltrates without evidence of demyelination or presence of SARS-CoV-2 by PCR test. This manuscript expands the spectrum of reported neuropathological changes in patients with COVID-19.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".