Thrombotic Microangiopathy in a Patient With COVID-19 Infection and Metastatic Cholangiocarcinoma
Bibliographic record
Abstract
This is a case report of a 63-year-old African American female with a past medical history most significant for metastatic cholangiocarcinoma that presented for evaluation of persistent shortness of breath. Initial workup was remarkable for refractory anemia, moderate schistocytes on peripheral smear and lab work suggestive of a hemolytic anemia. Due to concern for thrombotic thrombocytopenic purpura (TTP), she subsequently underwent several rounds of plasma exchange without significant improvement. Secondary to progressive renal failure, patient eventually had a renal biopsy with findings remarkable for thrombotic microangiopathy (TMA). Simultaneously, patient was also diagnosed with coronavirus disease 2019 (COVID-19) infection. After a few weeks of supportive care, she was stable for discharge. Unfortunately, she did become dialysis dependent. Prior to hospital admission, she was being treated for metastatic cholangiocarcinoma and had received chemotherapy with gemcitabine. Her last chemotherapy session was approximately 3 weeks prior to her first hospitalization. Furthermore, although her hemolytic work did suggest TMA, it was not consistent with the diagnosis of TTP. She was transferred to a tertiary care center where hemolytic labs were trended, and supportive care was maximized. In light of the current COVID-19 pandemic, it is crucial to further investigate the pathophysiology of TMA in patients with active malignancies and COVID-19 infections. To our knowledge, this is the first case of TMA in a patient with both metastatic cholangiocarcinoma and COVID-19 infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".