Cryofibrinogenemia After a Liver Transplant: First Reported Case Posttransplant and a Case-Based Review of the Nontransplant Literature.
Bibliographic record
Abstract
Cryofibrinogenemia is a rare disorder in which plasma, not serum, forms a cryoprecipitate. Patients with cryofibrinogenemia may be asymptomatic, or they may have painful ulcers, purpura, livedo reticularis, Raynaud phenomenon, perniosis of the extremities, thrombosis, and arthralgia. Cryofibrinogenemia may be primary or secondary to an underlying disorder such as connective tissue disease, malignancy, infection, drugs, or thromboembolic disease. Here, we present a 41-year-old woman with a pancreatic neuroendocrine tumor who underwent a Whipple procedure in 2003 followed by 2 liver transplants for hepatic metastases. Three years posttransplant, we discovered a biopsy-proven metastatic lesion in her femur. Five years posttransplant, she developed acute, severe pain in both feet, and was found to have cryofibrinogenemia despite immunosuppression post-transplant. Testing for connective tissue diseases and hematologic malignancy were negative. She was treated with high-dose prednisone, which completely resolved her symptoms. We also conducted a review of the literature via a PubMed search to summarize the association of cryofibrinogenemia with malignancy and treating cryofibrinogenemia with corticosteroids. Our study is the first reported case of cryofibrinogenemia that developed secondary to a neuroendocrine tumor posttransplant. Our report suggests that cryofibrinogenemia may occur despite immunosuppression adequate to prevent graft rejection, and that high-dose corticosteroids are an effective treatment for posttransplant cryofibrinogenemia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".