Acute Myeloid Leukemia Following Hodgkin Lymphoma: A Population-Based Study of 35 511 Patients
Bibliographic record
Abstract
Treatments for Hodgkin lymphoma are associated with large relative risks of acute myeloid leukemia (AML), but there are few estimates of the excess absolute risk (EAR), a useful measure of disease burden. One-year Hodgkin lymphoma survivors (N = 35,511) were identified within 14 population-based cancer registries in Nordic countries and North America from January 1, 1970, through December 31, 2001. We used Poisson regression analysis to model the EAR of AML, per 10,000 person-years. A total of 217 Hodgkin lymphoma survivors were diagnosed with AML (10.8 expected; unadjusted EAR = 6.2; 95% confidence interval = 5.4 to 7.1). Excess absolute risk for AML was highest during the first 10 years after Hodgkin lymphoma diagnosis but remained elevated thereafter. In subsequent analyses, adjusted for time since Hodgkin lymphoma diagnosis and presented for the 5-9 year interval, the EAR was statistically significantly (P < .001) larger in patients diagnosed with Hodgkin lymphoma at age 35 years and older than in those diagnosed before 35 years of age. The EAR of AML declined statistically significantly after 1984 (7.0 to 4.2 and 16.4 to 9.9 in the < 35 and > or = 35 age groups, respectively), which may be associated with modifications in chemotherapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".