Is COVID-19 Infection also a Silent Killer?
Bibliographic record
Abstract
A 59-year-old male had multiple comorbidities such as diabetes, dilated cardiomyopathy, hypertension, ischemic heart disease, and chronic obstructive pulmonary disease. He presented with dyspnea and had ground-glass opacity in the lungs. It was during the pandemic of COVID-19 so repeated Reverse transcription polymerase chain reaction (RT-PCR) was done, but all were negative. He got stabilized within 5 days and we planned discharge. Suddenly, he had right hemiplegia and developed altered sensorium. He had NIH Stroke Scale/Score of 28 and computed tomography-Alberta Stroke Program Early Computed Tomography Score of 10. We used tenecteplase (0.25 mg/kg bodyweight) for thrombolysis within 20 min of onset and planned mechanical thrombectomy for the occlusion of internal carotid artery and beyond. However, in magnetic resonance imaging of the brain, he had an established infarct in the left middle cerebral artery (MCA) territory (within this short time) without significant DWI/FLAIR mismatch. Hence, we continued conservative management. We incidentally detected him to have COVID-19 infection positivity on that day, but all inflammatory and coagulation parameters were normal on that day and later. His monitor did not reveal arrhythmia (during the event and later) and echocardiography failed to reveal evidence of culprit lesion. He had a rapid clinical decline, required hemicraniectomy but expired within 2 days. COVID-19 infection may have negative reports initially, but malignant MCA infarct with normal inflammatory markers makes our case special. The rapidity with which stroke developed underscores the severe nature of the disease process, the absence of arrhythmias (in this in-house stroke), and normal coagulation parameters hints that the exact mechanism of stroke in this type of infection is still an enigma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".