Inflammatory diseases in hematology: a review
Bibliographic record
Abstract
Hematopoietic cells are instrumental in generating and propagating protective inflammatory responses to infection or injury. However, excessive inflammation contributes to many diseases of the blood, bone marrow, and lymphatic system. We review three clinical categories of hematological inflammatory diseases in which recent clinical and translational advances have been made. The first category is monogenic inflammatory diseases. Genotype-driven research has revealed that previously mysterious diseases with protean manifestations are characterized by mutations that may be germline (e.g., deficiency of ADA2 or GATA2 deficiency) or somatic [e.g., vacuoles, enzyme E1, X-linked, autoinflammatory, somatic (VEXAS) syndrome]. The second category is the cytokine storm syndromes, including hemophagocytic lymphohistiocytosis, and Castleman disease. Cytokine storm syndromes are characterized by excessive production of inflammatory cytokines including interleukin-6 and interferon-γ, causing end-organ damage and high mortality. Finally, we review disorders associated with monoclonal and polyclonal hypergammaglobulinemia. The serum protein electrophoresis (SPEP) is typically ordered to screen for common diseases such as myeloma and humoral immunodeficiency. However, monoclonal and polyclonal hypergammaglobulinemia on SPEP can also provide important information in rare inflammatory diseases. For example, the autoinflammatory disease Schnitzler syndrome is notoriously difficult to diagnose. Although this orphan disease has eluded precise genetic or histological characterization, the presence of a monoclonal paraprotein, typically IgM, is an obligate diagnostic criterion. Likewise, polyclonal hypergammaglobulinemia may be an important early, noninvasive diagnostic clue for patients presenting with rare neoplastic diseases such as Rosai-Dorfman disease and angioimmunoblastic T-cell lymphoma. Applying these three categories to patients with unexplained inflammatory syndromes can facilitate the diagnosis of rare and underrecognized diseases.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".