Stem-cell transplantation in non-Hodgkinʼs lymphoma: improving outcome
Why this work is in the frame
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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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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) | 0.000 | 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 it