Leukemia and Myelodysplastic Syndrome in Granulomatosis with Polyangiitis: Subtypes, Clinical Characteristics, and Outcome
Bibliographic record
Abstract
OBJECTIVE: Previous studies have shown that patients with granulomatosis with polyangiitis (GPA) have an increased risk of hematological malignancies, especially leukemia. Our aim was to assess clinical characteristics and treatment of patients with GPA complicated by hematological malignancies with focus on leukemia and to describe these malignancies in more detail. METHODS: From the Swedish population-based patient register, all individuals with a diagnosis of GPA from 1964-2012 were identified (n = 3224). Through linkage with the Swedish Cancer Register, we searched for all cases of leukemia [International Classification of Diseases (ICD) 7: 204-207 and corresponding codes ICD 8-10] registered after the first discharge listing GPA. The GPA diagnosis was evaluated using the European Medical Association classification algorithm. To confirm the hematological malignancy, all diagnostic bone marrow samples were reclassified. Clinical data of both the GPA and hematological malignancy were collected from medical files. RESULTS: Twenty-one cases were identified, all of myeloid origin, including 9 with myelodysplastic syndrome developing to acute myeloid leukemia (MDS-AML), 7 AML, 3 MDS, and 2 chronic myeloid leukemia. The median time from GPA diagnosis to hematological malignancy was 8 years (range 5-21). All patients had severe generalized GPA and had received high doses of cyclophosphamide (CYC; median cumulative dose 96.5 g). Cytopenia occurred in 76% of the patients prior to the hematological malignancy. CONCLUSION: The findings emphasize the longterm risk of leukemia and MDS in CYC-treated, severely ill patients with GPA. Cytopenia during the course of GPA may be a warning sign and warrants a liberal attitude toward bone marrow examination.
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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) | 0.001 | 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".