Recommendations for Management of Secondary Antibody Deficiency in Multiple Myeloma
Bibliographic record
Abstract
Secondary antibody deficiency (SAD) is a subtype of secondary immunodeficiency characterized by low serum antibody concentrations (hypogammaglobulinemia) or poor antibody function. SAD is common in patients with multiple myeloma (MM) due to underlying disease pathophysiology and treatment-related immune system effects. Patients with SAD are more susceptible to infections and infection-related morbidity and mortality. With therapeutic advancements improving MM disease control and survival, it is increasingly important to recognize and treat the often-overlooked concurrent immunodeficiency present in patients with MM. The aims of this review are to define SAD and its consequences in MM, increase SAD awareness, and provide recommendations for SAD management. Based on expert panel discussions at a standalone meeting and supportive literature, several recommendations were made. Firstly, all patients with MM should be suspected to have SAD regardless of serum antibody concentrations. Patients should be evaluated for immunodeficiency at MM diagnosis and stratified into management categories based on their individualized risk of SAD and infection. Infection-prevention strategy education, early infection reporting, and anti-infective prophylaxis are key. We recommend prophylactic antibiotics or immunoglobulin replacement therapy (IgRT) should be considered in patients with severe hypogammaglobulinemia associated with a recurrent or persistent infection. To ensure an individualized and efficient treatment approach is utilized, patient's immunoglobin G concentration and infection burden should be closely monitored throughout treatment. Patient choice regarding route and IgRT treatment is also key in reducing treatment burden. Together, these recommendations and proposed management algorithms can be used to aid physician decision-making to improve patient outcomes.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".