Durable Remission in Hodgkin Lymphoma Treated With One Cycle of Bleomycin, Vinblastine, Dacarbazine and Two Doses of Nivolumab and Brentuximab Vedotin
Bibliographic record
Abstract
A 49-year-old woman with systemic lupus erythematosus, lupus nephritis and chronic congestive heart failure presenting with "bulky" cervical lymphadenopathy was diagnosed with classic Hodgkin lymphoma (HL) stage IIIB (positron emission tomography-computed tomography (PET-CT) scan and bone marrow biopsy). She received one cycle of bleomycin, dacarbazine, and vinblastine to debulk the tumor. Given her advanced heart failure, doxorubicin was not administered. After the first cycle of chemotherapy, she was switched to nivolumab plus brentuximab vedotin (BV) and received two doses 4 weeks apart, finishing in July 2019. A restaging PET-CT in June 2019 showed a complete remission (CR). After the second course of treatment, she was unable to tolerate more treatments and hence was placed on a surveillance program. She remains in CR after a follow-up of 3 years. This case highlights the role of a tailored treatment approach to optimize clinical outcomes in uniquely complex clinical circumstances. BV in combination with nivolumab is a reasonable alternative regimen in HL ineligible for cytotoxic chemotherapy.
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 | 0.000 | 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 | 0.001 | 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".