Bibliographic record
Abstract
Secondary hypogammaglobulinemia (SHG) is characterized by reduced immunoglobulin levels due to extrinsic causes, such as a medication or an acquired disease process, resulting in decreased immunoglobulin production or increased immunoglobulin loss. Most published reports of SHG refer to IgG hypogammaglobulinemia and data on isolated IgA or IgM hypogammaglobulinemia is limited. The common causes of SHG include medications, hematological malignancies, and conditions associated with protein loss. Hypogammaglobulinemia can increase the risk of infection, morbidity and mortality, particularly in patients who may already be immunocompromised due to their associated condition or use of immunosuppressive therapies. With growing use of immunosuppressive or immunomodulatory treatments that affect B-cells, it is increasingly important to assess and monitor for SHG. Treatment of the underlying condition or removal of the extrinsic factor often results in resolution of the SHG. A subset of patients presenting with autoimmune or malignant conditions can have a primary immunodeficiency (PID) or primary immune regulatory disorder. It is therefore important to consider both primary and secondary causes when assessing hypogammaglobulinemia. This article will review these common causes and discuss an approach to assessment and management of SHG.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".