Hematologic malignancies masquerading as rheumatologic diseases: A case series and review
Bibliographic record
Abstract
RATIONALE: Hematologic malignancies can mimic rheumatologic diseases, presenting a significant diagnostic challenge due to overlapping clinical features. This study highlights 5 cases of hematologic malignancies presenting as rheumatologic disorders and discusses the diagnostic complexities involved. PATIENT CONCERNS: The patients, aged 64 to 78, presented with diverse rheumatologic symptoms including polyarthritis, vasculitis, Raynaud phenomenon, and systemic symptoms such as weight loss, fatigue, and night sweats. Initial workups suggested rheumatologic diagnoses, leading to delays in recognizing the underlying malignancies. DIAGNOSES: The diagnostic journey involved extensive laboratory testing, imaging, and, in all cases, bone marrow biopsies, which ultimately revealed hematologic malignancies: angioimmunoblastic T-cell lymphoma (AITL), extranodal marginal zone lymphoma, myelodysplastic syndrome (MDS), and multiple myeloma. Misleading initial findings, such as autoimmune serologies and transient responses to immunosuppressive therapy, complicated the diagnostic process. INTERVENTIONS: Ultimately, the patients included in this case series benefited from hematological malignancy-specific therapies. Delayed diagnosis impacted the treatment course and outcomes. OUTCOMES: Outcomes varied: 2 patients achieved symptom control with targeted therapy, while others experienced complications such as infections or disease progression, ultimately leading to mortality in some cases. Patient frustrations underscored the psychologic toll of diagnostic delays. LESSONS: Hematologic malignancies can present as atypical or refractory rheumatologic diseases, emphasizing the need for vigilance in patients with unusual clinical courses. Early consideration of malignancy in differential diagnoses, especially with atypical serologic or histopathologic findings, is critical to improving outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".