OUTCOME AND TOXICITY PATTERNS IN CHILDREN AND ADOLESCENTS WITH NON-HODGKIN LYMPHOMA: A SINGLE INSTITUTION EXPERIENCE
Bibliographic record
Abstract
Background: The incidence and biology of non-Hodgkin lymphoma (NHL) vary according to age. Some data suggest that the impact of age in pediatric and adolescent NHL patients depends on the histological subtype. Objectives: We aimed to analyze the impact of age at diagnosis on clinical characteristics and treatment-related toxicity in children and adolescents with NHL.Methods: Retrospective review of medical records of children and adolescents diagnosed with NHL at the Hospital for Sick children, Toronto, between January 1995 and December 2008.Results: 164 children were diagnosed with NHL during the study period, with a median age at diagnosis of 10 years. With a median follow-up of 6.2 years, 5-year OS in patients aged <15 and 15-18 years was 89± 2% vs 82% ± 6%, respectively (P = 0.30), and 5-year EFS was 84% ± 3% vs. 77% ± 7% (P= 0.37). In Burkitts lymphoma (BL) and lymphoblastic lymphoma (LL) there was a trend towards better outcomes in children compared to adolescents, with EFS of 91% ± 4% vs. 75% ± 15%, respectively in BL (P= 0.17), and 82% ± 7% vs. 51.4% ± 2% respectively in LL (P= 0.16). Late effects occurred in 21 patients (12.8%).Conclusions: Children with NHL aged < 15 years tend to have better survival rates and less long-term toxicity than adolescents aged 15-18 years.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".