Integration of Pediatric Hodgkin Lymphoma Treatment and Late Effects Guidelines: Seeing the Forest Beyond the Trees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it