Thrombotic microangiopathy in a patient with adult‐onset <scp>S</scp>till's disease
Bibliographic record
Abstract
BACKGROUND: Since there are many disorders that can present with thrombotic microangiopathy (TMA), establishing a correct diagnosis is important to offer the most appropriate therapy. CASE REPORT: A 26-year-old woman was transferred to our hospital with fragmentation hemolytic anemia, thrombocytopenia, and acute kidney failure. History revealed that she was recently diagnosed with adult-onset Still's disease (AOSD) and received intraocular injections of bevacizumab to treat acute retinal artery occlusion. At our hospital, she underwent extensive investigations and was treated with high-dose steroids, hemodialysis, and therapeutic plasma exchange. For recurrent disease, she received a single dose of eculizumab. RESULTS: The patient's ADAMTS13 activity was normal and she had evidence of complement activation. Genetic testing identified a benign polymorphism in the C3 gene. Pathophysiology of TMA in AOSD is briefly discussed and an overview of the literature is presented. CONCLUSION: Work-up of a new fragmentation hemolytic anemia and thrombocytopenia should include careful review of past history, including medications, as well as relevant laboratory investigations with aim to establish a correct diagnosis. Occasionally, the correct diagnosis is not the obvious one and there could be multiple contributors to the pathogenesis. Establishing diagnosis is important for counseling patient on disease prognosis and to guide treatment.
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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) | 0.001 | 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 | 0.000 |
| 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".