Antibody-Negative Paraneoplastic Autoimmune Multiorgan Syndrome (PAMS) in a Patient with Follicular Lymphoma Accompanied by an Excess of Peripheral Blood CD8+ Lymphocytes
Bibliographic record
Abstract
Paraneoplastic autoimmune multiorgan syndrome (PAMS) is a life-threatening autoimmune disease associated with malignancies. Here, we present a patient initially misdiagnosed with "chronic" Stevens-Johnson syndrome. Over a year later, the patient was diagnosed with stage IV follicular lymphoma and treated with an anti-CD20 antibody. At this time, his skin condition had significantly worsened, with erythroderma and massive mucosal involvement, including in the mouth, nose, eyes, and genital region. Histopathology revealed lichenoid infiltrates with interface dermatitis, dyskeratoses, necrotic keratinocytes, and a dense CD8+ infiltrate with strong epidermotropism. Direct and indirect immunofluorescence tests for autoantibodies were negative. Remarkably, we retrospectively discovered a chronic increase in peripheral CD8+ lymphocytes, persisting for over a year. Consequently, the patient was diagnosed with antibody-negative PAMS. Three weeks later, he succumbed to respiratory failure. This dramatic case highlights the challenges in diagnosing PAMS, particularly in cases where immunofluorescence assays are negative. Importantly, we observed, for the first time, a chronic excess of CD8+ peripheral blood lymphocytes, associated with PAMS, consistent with the systemic, autoreactive T-cell-driven processes that characterize this condition.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".