Stem-cell transplantation in non-Hodgkinʼs lymphoma: improving outcome
Bibliographic record
Abstract
High-dose therapy with stem-cell transplantation is a potentially curative therapy for younger patients with relapsed aggressive non-Hodgkin's lymphoma (NHL) and is also under investigation in relapsed indolent NHL. There are, however, risks associated with this treatment strategy. Autologous stem-cell transplantation (ASCT) continues to be associated with a high risk of relapse, while graft-versus-host disease is a major limiting factor with allogeneic stem-cell transplantation. The presence of minimal residual disease (MRD) in the harvested, re-infused stem cells, or remaining in the patient following chemotherapy, is associated with relapse after ASCT. As a result, monitoring and eradicating MRD has become a major focus of many studies in NHL. Rearrangement and overexpression of the bcl-1 and bcl-2 genes are the hallmarks of mantle-cell and follicular lymphoma, respectively, and evidence suggests that they are promising surrogate markers of MRD. Polymerase chain reaction analysis is a sensitive methodology used to monitor the status of occult lymphoma cells bearing these genetic aberrations, and results from trials of ASCT have shown that clearance of bcl-1/JH- and bcl-2/JH-positive cells following treatment is associated with a significant improvement in outcome. Rituximab, the anti-CD20 monoclonal antibody, is increasingly used for in vivo purging and can effectively eradicate bcl-1/JH- and bcl-2-positive cells. If the encouraging preliminary results with rituximab are maintained with a longer follow-up, this agent could play a pivotal role in improving outcome after stem-cell transplantation in NHL.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".