The Role of Immune Checkpoint Inhibitors in Classical Hodgkin Lymphoma
Bibliographic record
Abstract
Hodgkin Lymphoma (HL) is a unique disease entity both in its pathology and the young patient population that it primarily affects. Although cure rates are high, survivorship can be linked with significant recent long-term morbidity associated with both chemotherapy and radiotherapy. The most significant advances have been with the use of the anti-CD30-drug conjugated antibody brentuximab vedotin (BV) and inhibitors of program death 1 (PD-1). HL is genetically wired to up-regulate program death ligand 1 (PD-L1) in >95% of cases, creating a state of so-called “T cell exhaustion”, which can be reversed with immune checkpoint-inhibitor blockade. The overall and complete response rates to PD-1 inhibitors in patients with relapsed or refractory HL are 70% and 20%, respectively, with a long median duration of response of ~16 months. In fact, PD-1 inhibitors can benefit a wide spectrum of relapsed HL patients, including some who have “progressive disease” by strict response criteria. We review the biology of HL, with a focus on the immune micro-environment and mechanisms of immune evasion. We also provide the rationale supporting the use of PD-1 inhibitors in HL and highlight some of the challenges of monitoring disease response in patients treated with this immunotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".