Insufficient evidence exists to use histopathologic subtype to guide treatment of idiopathic multicentric Castleman disease
Bibliographic record
Abstract
Idiopathic multicentric Castleman disease (iMCD) is a rare immunologic disorder characterized by systemic inflammation, multicentric lymphadenopathy, and organ dysfunction. Enlarged lymph nodes demonstrate a spectrum of characteristic but variable histopathologic features historically categorized into hyaline vascular (HV) (or hypervascular [HyperV] more recently), plasmacytic, or "mixed." Though the etiology is unknown, a pro-inflammatory cytokine storm, often involving interleukin-6 (IL-6), contributes to pathogenesis. Anti-IL-6 therapy with siltuximab is the only FDA- or EMA-approved treatment based on efficacy and safety in multiple studies. Importantly, no patients considered to have HV histopathology achieved the primary endpoint in the Phase II study. NCCN currently recommends siltuximab first-line for iMCD, except for patients considered to have HV histopathology. We investigated whether histopathologic subtype should guide siltuximab treatment decisions. Secondary analyses of clinical trial and real-world data revealed similar clinical benefit across histopathologic subtypes. Notably, only 18 of 79 patients in the Phase II study were consistently classified into histopathologic subtype by three independent review panels, demonstrating limited reliability to guide treatment decisions. Real-world data further demonstrate siltuximab's effectiveness in patients considered to have HV (or HyperV). Though histopathology is a critical component for diagnosis, there is insufficient evidence to guide treatment based solely on lymph node histopathologic subtype.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".