Bibliographic record
Abstract
The evolution of treatment for classical Hodgkin lymphoma (cHL) represents a great success in oncology, with disease outcomes evolving from universally fatal to vastly curable. However, not all patients benefit equally from modern therapies, which include response‑adapted regimens and the addition of novel, targeted agents to the front-line setting. Although patients older than 60 years account for the later peak in cHL’s characteristic bimodal age distribution and represent approximately 20–25% of all patients with cHL, their outcomes remain inferior compared to younger patients. A retrospective study including 401 patients >60 years treated in British Columbia between 2000 and 2019 revealed modest progression‑free survival (PFS) and disease-specific survival rates of 50% and 63%, respectively, with a median follow-up of nine years. While these outcomes have improved relative to cohorts treated prior to 2000, they nevertheless fall short of those experienced by younger patients. Furthermore, the gap in outcomes between young and older patients progressively worsens with each increasing age decile, with patients >70 years having a particularly poor prognosis. This shortfall has been attributed in part to patient-specific factors such as comorbidities and frailty, which may limit treatment tolerance, but also to differing disease biology, with negative prognostic features including advanced stage disease, Epstein-Barr virus positivity, and mixed cellularity histology often present in those with older age. Adding to the challenges in treating older patients is the fact that this group is frequently underrepresented in clinical trials, or excluded altogether, making their optimal treatment ill-defined.
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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 | 0.000 | 0.000 |
| 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".