Coronavirus Disease 2019-Associated Thrombotic Microangiopathy
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has spread tremendously since its first appearance in December 2019. Infected individuals can experience a wide range of systemic complications, including thrombotic microangiopathy (TMA). Like the other forms of TMA, COVID-19-associated TMA is characterized by thrombocytopenia, hemolytic anemia, and organ failure (such as acute kidney injury). The role of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in COVID-19-associated TMA is most probably dual: it can act either as a trigger to an underlying condition or as a cause of TMA. As opposed to the majority of other reported cases, it may be that in our case COVID-19 was the only cause of TMA. We present a case of a 32-year-old previously healthy man who was treated for acute kidney injury associated with TMA, which we believe was caused by COVID-19. Thrombotic thrombocytopenic purpura, as well as other possible known causes of typical and atypical hemolytic-uremic syndrome, was excluded. During his hospitalization, three negative nasopharyngeal swabs for SARS-CoV-2 were obtained, but serological tests showed the presence of IgG and IgA antibodies. After initial treatment known to be helpful in other forms of TMA (therapeutic plasma exchange and methylprednisolone), his renal function and platelet count recovered completely. Our case illustrates the importance of quickly recognizing this life-threatening complication of COVID-19 and using treatment that has been shown to be beneficial in other forms of TMA. Future studies of the pathophysiology and subsequent targeted treatment of this novel disease are needed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".