Asymptomatic COVID-19-Associated Acquired Hemophilia A and Disseminated Intravascular Coagulation From a Bypassing Agent
Bibliographic record
Abstract
Acquired hemophilia A (AHA) is a clotting disorder characterized by the presence of neutralizing antibodies that inhibit factor VIII, resulting in increased bleeding risk. Known etiologies include malignancy, autoimmune conditions, graft-vs-host disease, and more recently coronavirus disease 2019 (COVID-19) infection. In this case report, we describe an 86-year-old female who was found to have AHA incidentally during preoperative workup for meningioma resection. She was subsequently found to have COVID-19 infection which was the likely cause of her development of AHA. She was treated with factor eight inhibitor bypassing agent (FEIBA) and recombinant factor VII (rVII) for a small hematoma on her right arm along with prednisone and cyclophosphamide. She then developed disseminated intravascular coagulation (DIC) initially secondary to FEIBA and subsequently rFVII. DIC resolved after these factor concentrates were withheld. The aim of this case report was to emphasize the importance of monitoring partial thromboplastin time (PTT) in patients with COVID-19 and proceeding with AHA workup if indicated. It is also imperative to know and understand the potentially life-threatening, albeit rare, adverse effects of DIC associated with the administration of factor concentrates, especially in the elderly population and withholding these factor concentrates once DIC is suspected.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".