Non-Hodgkin lymphoma and pre-existing conditions: spectrum, clinical characteristics and outcome in 213 children and adolescents
Bibliographic record
Abstract
Children and adolescents with pre-existing conditions such as DNA repair defects or other primary immunodeficiencies have an increased risk of non-Hodgkin lymphoma. However, large-scale data on patients with non-Hodgkin lymphoma and their entire spectrum of pre-existing conditions are scarce. A retrospective multinational study was conducted by means of questionnaires sent out to the national study groups or centers, by the two largest consortia in childhood non-Hodgkin lymphoma, the European Intergroup for Childhood non-Hodgkin Lymphoma, and the international Berlin-Frankfurt-Münster Study Group. The study identified 213 patients with non-Hodgkin lymphoma and a pre-existing condition. Four subcategories were established: a) cancer predisposition syndromes (n=124, 58%); b) primary immunodeficiencies not further specified (n=27, 13%); c) genetic diseases with no increased cancer risk (n=40, 19%); and d) non-classifiable conditions (n=22, 10%). Seventy-nine of 124 (64%) cancer predispositions were reported in groups with more than 20 patients: ataxia telangiectasia (n=32), Nijmegen breakage syndrome (n=26), constitutional mismatch repair deficiency (n=21). For the 151 patients with a known cancer risk, 5-year event-free survival and overall survival rates were 40%±4% and 51%±4%, respectively. Five-year cumulative incidences of progression/relapse and treatment-related death as a first event were 22%±4% and 24%±4%, respectively. Ten-year incidence of second malignancy was 24%±5% and 7-year overall survival of the 21 patients with a second malignancy was 41%±11%. Patients with non-Hodgkin lymphoma and pre-existing conditions have an inferior survival rate with a large proportion of therapy-related deaths compared to patients with non-Hodgkin lymphoma and no pre-existing conditions. They may require special vigilance when receiving standard or modified/reduced-intensity chemotherapy or when undergoing allogeneic stem cell transplantation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".