Rheumatic manifestations of hematologic malignancies: Correlation with
Bibliographic record
Abstract
Objective: To determine the rheumatic manifestations associated with hematologic malignancies namely acute and chronic leukemia, Hodgkin’s and non-Hodgkin’s lymphomas, and multiple myeloma; and the markers correlating with their presence. Methods: Eighty patients with hematologic malignancies (28 leukemia, 28 lymphoma and 24 multiple myelomas) were evaluated and examined for the presence of rheumatic manifestations, clinically, radiologically, and by bone scan. Two groups of controls: clinical and laboratory (80 and 15 healthy individuals from the normal population respectively) were also studied. Routine laboratory tests and bone marrow aspiration were done for all patients, while serum rheumatoid factor (RF), antinuclear antibodies (ANA), creatine phosphokinase (CPK), and serum beta-2 macroglobulin were assessed as a marker for rheumatic manifestations in patients and controls. Results: Rheumatic manifestations were identified in 50 patients with hematologic malignancies (62.5%) and 21 clinical controls (26.3%) (p< 0.001, odds ratio=4.7, and 95% confidence interval=2.4-9.2). Arthralgia and low back pain were the most significantly rheumatic manifestations associated with hematological malignancies in comparison with healthy controls (OR=15.4, and OR=3.4 respectively). Serum beta-2 macroglobulin was elevated in 38 patients (47.5%), rheumatoid factor was positive in 30 patients (37.5%), and ANA was found in 19 (23.7%) with a significant difference between patients and laboratory controls. 60 patients (75%) had radiological findings and 19 patients (23.75%) had an increased uptake in bone scan. Serum beta-2 macroglobulin was positively correlated with rheumatic manifestations (r=0.21, p=0.02), osteopenia in x-rays (r=0.24, p=0.03), and increased uptake in bone scan (r=0.41, p< 0.001). Conclusion: Rheumatic manifestations occur in 62.5% of patients with leukemia, lymphoma, and myeloma, significantly more commonly than in age and sex-matched controls. They may precede the other manifestations of malignancy (31.3%), occur during the course of illness (25%) or follow as a complication of chemotherapy (6.3%). Serum beta-2 macroglobulin is a useful laboratory test, being a marker for the presence of rheumatic manifestations and predicting osteopenia in x-rays and increased uptake in bone scan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".