New Treatment Algorithms in Hodgkin Lymphoma: Too Much or Too Little?
Bibliographic record
Abstract
Hodgkin lymphoma treatment continues to evolve as new means of assessing response to treatment, new appreciation of important risk factors, and more effective therapeutic agents become available. Treatment algorithms integrating functional imaging now provide the opportunity to modify therapy during its delivery, allowing adjustment of duration and intensity of chemotherapy and rationale identification of patients who may benefit from the addition of therapeutic irradiation. Novel agents, including the antibody drug conjugate brentuximab vedotin and checkpoint inhibitors such as nivolumab and pembrolizumab can improve the effectiveness of treatment while keeping toxicity within acceptable limits. Carefully designed clinical trials permit the identification of superior approaches in which efficacy is enhanced and toxicity minimized. Clinicians treating patients with Hodgkin lymphoma now have access to novel treatment approaches, which will require detailed assessment of each patient and careful discussion of the goals and risks of treatment at the time of planning primary treatment, again during delivery of that treatment as data indicating ongoing effectiveness become available, at the conclusion of initial intervention, and, when the need arises, at the time of recurrence of disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".