COVID-19 Presenting With a Challenging Combination of Thrombocytopenia and Thrombosis
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) causes various hematological abnormalities, leading to several complications in the disease course. We report two COVID-19 cases presenting with a combination of thrombocytopenia and coagulopathy complications in late 2020. A 73-year-old male with a history of immune thrombocytopenic purpura (ITP) presented with acute ischemic stroke and acute thrombocytopenia in the setting of COVID-19. He was managed with steroids and intravenous immunoglobulin (IVIG) and had a subsequent acute ischemic stroke with microhemorrhages. Another 72-year-old female with a history of cryptogenic liver cirrhosis and chronic thrombocytopenia presenting with acute thrombocytopenia in the setting of COVID-19 was managed with steroids and IVIG. She had a coagulopathic complication of deep venous thrombosis (DVT) later in her disease course managed with inferior vena cava filter and low-dose enoxaparin, but she subsequently died with a bleeding complication of retroperitoneal hemorrhage. Despite the aggressive ongoing research, the treatment options for severe COVID-19 are limited to date and the mortality remains high. Both these cases are examples of challenging situations that the physicians are currently facing with COVID-19 pandemic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".