Integration of Pediatric Hodgkin Lymphoma Treatment and Late Effects Guidelines: Seeing the Forest Beyond the Trees
Bibliographic record
Abstract
The successful integration of clinical trials into pediatric oncology has led to steady improvement in the 5-year survival rate for children diagnosed with Hodgkin lymphoma (HL). It is estimated that >95% of children newly diagnosed with HL will become long-term survivors. Despite these successes, survival can come at a cost. Historically, long-term survivors of HL have a high risk of late-occurring adverse health effects and increased risk of nonrelapse mortality compared with the general population. The recognition of late-occurring events paired with the decades of life remaining for children cured of HL have made paramount the need to develop effective treatments that minimize the risk of late toxicity. Toward this goal, multiple, dose-intense, risk- and response-based regimens that use lower cumulative doses of chemotherapy and radiation have been developed. Appropriate frontline treatment selection requires a level of familiarity with the efficacy, acute toxicity, convenience, and late effects of treatments that may be impractical for providers who infrequently treat children with HL. There is an increasing need for guideline developers to begin to merge considerations from both frontline treatment and survivorship guidelines into practical documents that integrate potential long-term health risks. Herein, we take the first steps toward doing so by aligning cumulative treatment exposures, anticipated risks of late toxicity, and suggested surveillance recommendations for NCCN-endorsed Pediatric HL Guidelines. Future studies that integrate simulation modeling will strengthen this integrated approach and allow for opportunities to incorporate regimen-specific risks, health-related quality of life, and cost-effectiveness into decision tools to optimize HL therapy.
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".