Stage I-II nodular lymphocyte-predominant Hodgkin lymphoma: a multi-institutional study of adult patients by ILROG
Bibliographic record
Abstract
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is an uncommon histologic variant, and the optimal treatment of stage I-II NLPHL is undefined. We conducted a multicenter retrospective study including patients ≥16 years of age with stage I-II NLPHL diagnosed from 1995 through 2018 who underwent all forms of management, including radiotherapy (RT), combined modality therapy (CMT; RT+chemotherapy [CT]), CT, observation after excision, rituximab and RT, and single-agent rituximab. End points were progression-free survival (PFS), freedom from transformation, and overall survival (OS) without statistical comparison between management groups. We identified 559 patients with median age of 39 years: 72.3% were men, and 54.9% had stage I disease. Median follow-up was 5.5 years (interquartile range, 3.1-10.1). Five-year PFS and OS in the entire cohort were 87.1% and 98.3%, respectively. Primary management was RT alone (n = 257; 46.0%), CMT (n = 184; 32.9%), CT alone (n = 47; 8.4%), observation (n = 37; 6.6%), rituximab and RT (n = 19; 3.4%), and rituximab alone (n = 15; 2.7%). The 5-year PFS rates were 91.1% after RT, 90.5% after CMT, 77.8% after CT, 73.5% after observation, 80.8% after rituximab and RT, and 38.5% after rituximab alone. In the RT cohort, but not the CMT cohort, variant immunoarchitectural pattern and number of sites >2 were associated with worse PFS (P < .05). Overall, 21 patients (3.8%) developed large-cell transformation, with a significantly higher transformation rate in those with variant immunoarchitectural pattern (P = .049) and number of involved sites >2 (P = .0006). OS for patients with stage I-II NLPHL was excellent after all treatments.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".