Management of Hodgkin Lymphoma in Relapse after Autologous Stem Cell Transplant
Bibliographic record
Abstract
Recurrence of Hodgkin lymphoma (HL) occurs in about 50% of patients after autologous stem cell transplantation (ASCT), usually within the first year, and represents a significant therapeutic challenge. The natural history of recurrent HL in this setting may range from a rapidly progressive to a more indolent course. Patients in this setting are often young, without comorbidities and able to tolerate additional therapies: expectations are often still high. The approach to treatment depends on clinical variables (time to relapse, perceived sensitivity to additional cytotoxic therapy, disease stage), prior history of radiation therapy, the availability of an HLA-identical donor, and the availability of new agents via clinical trials. Although very few of these patients can be cured, results from reported series, albeit often small and sometimes with relatively short follow-up, document that excellent disease control can be achieved with radiation, single or multiagent chemotherapy, and reduced-intensity allogeneic transplantation. The results of these approaches will be reviewed, and a treatment algorithm incorporating the use of standard or investigational agents or approaches will be discussed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".