Rational Use of Immunoglobulin in Adult Patients with Secondary Hypogammaglobulinemia in the Setting of Hematologic Malignancy: A Canadian Perspective
Bibliographic record
Abstract
Hypogammaglobulinemia is identified by the detection of low serum immunoglobulin (Ig) levels. Secondary hypogammaglobulinemia (SHG) is an acquired state in which circulating Ig levels are reduced due to suppressed antibody production or increased antibody loss. Specifically, SHG most commonly refers to low circulating total IgG levels. In contrast, primary hypogammaglobulinemia (PHG) is due to an underlying inborn error of immunity contributing to low or defective Ig production and frequent and/or severe infections. In patients with hematologic malignancies, it is important to evaluate baseline Ig (IgG, IgM, IgA) levels at the time of diagnosis. However, it may be challenging to distinguish whether low Ig levels are attributable to PHG or SHG, if antibody defects are identified in the context of hematologic malignancies (including chronic lymphocytic leukemia [CLL], lymphoma, and multiple myeloma), even before initiation of immunosuppressive treatment. PHG should be considered, especially in younger patients presenting with a hematologic malignancy who have a history of recurrent, severe infections. Treatment of several hematologic malignancies includes anti-CD20 B cell‑depleting therapy, which is known to cause the development of SHG. Advancements in lymphoma and myeloma management now incorporate bispecific antibody therapies and chimeric antigen receptor T-cell (CAR T-cell) therapies, which have revolutionized care for patients with disease refractory to conventional treatments. However, the risk of SHG is significant, with rates of ≥70% with bispecific antibody treatments and 20–46% with CAR T-cell therapies. Thus, patients with hematologic malignancies have a high rate of SHG attributable to both the underlying disease and associated 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".