Neuroimaging Findings of Hospitalized Covid-19 Patients: A Canadian Retrospective Observational Study
Bibliographic record
Abstract
Purpose: Coronavirus disease (COVID-19) has been associated with neurologic sequelae and neuroimaging abnormalities in several case series previously. In this study, the neuroimaging findings and clinical course of adult patients admitted with COVID-19 to a tertiary care hospital network in Canada were characterized. Methods: This is a retrospective observational study conducted at a tertiary hospital network in Ontario, Canada. All adult patients with PCR-confirmed COVID-19 admitted from February 1, 2020 to July 22, 2020 who received neuroimaging related to their COVID-19 admission were included. CT and MR images were reviewed and categorized by fellowship-trained neuroradiologists. Demographic and clinical data were collected and correlated with imaging findings. Results: We identified 422 patients admitted with COVID-19 during the study period. 103 (24.4%) met the inclusion criteria and were included: 30 ICU patients (29.1%) and 73 non-ICU patients (70.9%). A total of 198 neuroimaging studies were performed: 177 CTs and 21 MRIs. 17 out of 103 imaged patients (16.8%) had acute abnormalities on neuroimaging: 10 had macrohemorrhages (58.8%), 9 had acute ischemia (52.9%), 4 had SWI abnormalities (23.5%), and 1 had asymmetric sulcal effacement suggesting possible focal encephalitis (5.8%). ICU patients were more likely to have positive neuroimaging findings, more specifically acute ischemia and macrohemorrhages ( P < 0.05). Macrohemorrhages were associated with increased mortality ( P < 0.05). Conclusion: Macrohemorrhages, acute ischemia and SWI abnormalities were the main neuroimaging abnormalities in our cohort of hospitalized COVID-19 patients. Acute ischemia and hemorrhage were associated with worse clinical status.
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How this classification was reachedexpand
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".