Management of Mixed Warm/Cold Autoimmune Hemolytic Anemia: A Case Report and Review of Current Literature
Bibliographic record
Abstract
Background. Mixed warm/cold autoimmune hemolytic anemia (AIHA) is a rare diagnostic entity with limited therapeutic options. Previous literature has described the diagnostic difficulty in this pathology and the limited response rates to corticosteroids. Furthermore, there is limited evidence regarding the use of rituximab in this condition. Methods. Alongside our case report, we conducted a scoping review of case reports/case series describing mixed AIHA, their treatment, and clinical outcomes since 2000. Inclusion criteria included a confirmed diagnosis of mixed AIHA (confirmed warm antibodies and cold agglutinins based on DAT). Case Summary/Results. We present a case of mixed AIHA in an 83-year-old female presenting with extensive, bilateral pulmonary embolisms and left renal vein thrombosis. The patient underwent extensive workup with no identifiable provoking etiology. Initial treatment involved prednisone therapy was transitioned to rituximab upon diagnosis of mixed AIHA. The patient demonstrated a mixed response with stable hemoglobin and transfusion independence; however, with persistently elevated hemolytic indices following completion of rituximab treatment. Our literature review identified 16 articles; two were excluded for unavailable clinical details. The most commonly associated conditions included autoimmune conditions (n = 5, 26%) and lymphoproliferative disorders (n = 3, 12%). The most common treatment involved corticosteroids; seven studies involved the use of rituximab. Conclusion. Mixed AIHA represents a complex diagnosis and optimal management is not well established. Consistent with our case, recent literature suggests a promising response to rituximab and a limited response to steroid treatment. Given the limited literature, additional studies are required to elucidate optimal management of this unique pathology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".