Comparison of Survivorship Care Guidelines for Patients With Lymphoma: Recommendations for Harmonization and Future Research Agenda
Bibliographic record
Abstract
PURPOSE Lymphomas are a heterogeneous group of diseases that develop in individuals of all ages and have variable prognoses. Improved survival resulting from therapy advances has led to the emergence of diverse late effects. Although several (US)–based organizations have developed survivorship guidelines, the distinct features of lymphoma subtypes and diverse therapies used raise concerns regarding their applicability to lymphoma survivors. We compared survivorship recommendations (outside primary disease monitoring) between US clinical guidelines. METHODS We extracted information from 17 guidelines from five US-based organizations: ASCO (n = 11), American Cancer Society (n = 1), Children's Oncology Group (n = 1), Center for International Blood and Marrow Transplant Research (n = 1), and the National Comprehensive Cancer Network (n = 3). Guidelines were evaluated to determine whether they offer recommendations on physical effects , psychosocial and quality of life ( QOL ), and health promotion and prevention . Comparisons were focused on second primary malignancy, cardiovascular complications, and vaccination. RESULTS Survivorship recommendations on physical effects and psychosocial and QOL mainly differ in the timing and approaches for screening. Vaccination recommendations were primarily derived from other cancer populations. Identified research gaps were a lack of understanding of the risk of late effects across lymphoma subtypes, the role of social determinants of health in survivorship, and the lack of a survivorship care model that integrates lymphoma subtypes and treatment exposures. CONCLUSION This study raises awareness about the complexity and challenges of managing survivors under the umbrella diagnosis of lymphoma. The inconsistency and incompleteness of existing guidelines may lead to suboptimal survivorship care. We propose expert-based research priorities to address gaps and unmet needs to help develop risk-based follow-up recommendations to optimize survivorship care for lymphoma survivors.
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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 | 0.342 | 0.563 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".