How I treat relapsed and refractory Hodgkin lymphoma
Bibliographic record
Abstract
Relapsed or refractory Hodgkin lymphoma is a challenging problem for clinicians who treat hematologic malignancies. The standard management of these patients should include the use of salvage chemotherapy followed by autologous stem cell transplant (ASCT) in patients who are chemotherapy sensitive. Open issues in this area include the role of functional imaging, the specific chemotherapy regimen to be used before ASCT, and the role of consolidative radiotherapy. Some patients will not be eligible for ASCT, and alternative approaches with conventional chemotherapy alone or with salvage radiotherapy should be considered. Prognostic factors for relapsed/refractory disease have been identified but generally are not used as a part of risk-adapted therapy. Allogeneic transplantation may offer the potential of a graft-versus-lymphoma effect, but this therapy has significant toxicity and results in few long-term disease-free survivors; hence, it should only be offered in the context of disease-specific clinical trials. An expanding list of novel drugs has exhibited promising single-agent activity. Patients have effective options beyond primary therapy, and continued progress through controlled trials remains a tangible goal in the treatment of relapsed and refractory disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".