The evolving management algorithm for the patient with newly diagnosed cold agglutinin disease
Bibliographic record
Abstract
INTRODUCTION: Cold agglutinin disease (CAD) is driven by IgM autoantibodies reactive at <37°C and able to fix complement. The activation of the classical complement pathway leads to C3-mediated extravascular hemolysis in the liver and to intravascular hemolytic crises in case of complement amplifying conditions. C3 positivity at direct Coombs test along with high titer agglutins are required for the diagnosis. Treatment is less standardized. AREAS COVERED: This review recapitulates CAD diagnosis and then focus on the evolving management of the disease. Both current approach and novel targeted drugs are discussed. Literature search was conducted in PubMed and Scopus from 2000 to 2024 using 'CAD' and 'autoimmune hemolytic anemia' as keywords. EXPERT OPINION: Rituximab represents the frontline approach in patients with symptomatic anemia or disabling cold-induced peripheral symptoms and is effective in 50-60% of cases. Refractory/relapsing patients are an unmet need and may now benefit from complement inhibitors, particularly the anti-C1s sutimlimab, effective in controlling hemolysis thus improving anemia in >80% of patients, but not active on cold-induced peripheral symptoms. Novel drugs include long-acting complement inhibitors, plasma cells, and B-cell targeting agents (proteasome inhibitors, anti-CD38, BTKi, PI3Ki, anti-BAFF). Combination therapy may be the future answer to CAD unmet needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".