Incidence and mortality of second primary malignancies after lymphoma: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Second primary malignancies (SPMs) account for an increasing proportion of human malignancies. We estimated the incidence, risk factors and outcomes in lymphoma survivors with SPMs. METHODS: Patients diagnosed with SPMs after primary lymphoma from 2010 to 2021 were included in this study. The incidence, mortality and clinical characteristics of SPMs in our center and Surveillance, Epidemiology, and End Results database were delineated and analyzed. Standardized incidence ratio quantified second cancer risk. RESULTS: A total of 2912 patients of lymphoma were included, 63 cases of SPM met the inclusion criteria, with the prevalence of SPMs after lymphoma was 2.16%. The male-to-female ratio of 2.32:1. The majority of these patients were older (≥60 years old, 61.90%) and previously treated with chemotherapy (68.25%). The common types among SPMs were digestive system tumors (42.86%), respiratory system tumors (20.63%) and urinary system tumors (12.70%). Additionally, cancer risks were significantly elevated after specific lymphoma though calculating the expected incidence. In terms of mortality, the diagnosis of SPMs was significantly associated with an increased risk of death over time. Moreover, although the outcome was favorable in some SPM subtypes (thyroid and breast cancer), other SPMs such as stomach and lung tumors had a dismal prognosis. CONCLUSION: With the improvement of medical standards, the survival of lymphoma patients has been prolonged. However, the incidence of SPM is increasing, particularly among men and older lymphoma survivors. Therefore, more attention should be invested in the SPM to further improve the prognosis of these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".