Second Cancers in Survivors of Hodgkin Lymphoma: Risks and Recommendations
Bibliographic record
Abstract
Survivors of Hodgkin lymphoma (HL) who received certain oncology treatment years ago may have a lifelong risk for second cancers. This article reviews evidence-based data about subsequent solid tumor development in HL survivors. Regarding the development of solid cancer, a significant difference existed between one study group of HL survivors and same-aged people from the general population. HL treatments using combinations of radiation and chemotherapy and those using extended-field radiation have been suggested to pose an increased risk for second cancers. Changes in treatment for HL reflect researchers' attempts to reduce late complications of oncology treatment. Oncology nurses are in a unique position to counsel patients with HL and survivors regarding the importance of follow-up assessments, cancer-prevention practices, and screening recommendations. Ultimately, results will ensure that HL survivors have a better chance of wellness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".