Allogeneic stem cell transplantation for renal cell carcinoma
Bibliographic record
Abstract
Although the prognosis for patients with metastatic kidney cancer remains poor, a number of promising immunotherapeutic approaches for the treatment of metastatic disease have been developed over the past decade. The response of some patients to cytokines such as interleukin-2 and interferon-alpha, and more recently, vaccination with dendritic cell/tumor fusions has laid the ground work for ongoing immune-based investigational approaches. Allogeneic stem cell transplantation is a potent form of immunotherapy capable of delivering potentially curative immune-mediated anti-tumor effects against a number of different hematological malignancies. Knowledge of renal cell carcinoma's unusual susceptibility to immune attack has led to the hypothesis that tumor rejection, mediated through immunocompetent donor T-cells, might be generated against this solid tumor following the transplantation of an allogeneic immune system. Although clinical trials are early and ongoing, the recent observation of metastatic disease regression following non-myeloablative stem cell transplantation has identified renal cell carcinoma as being susceptible to a graft-versus-tumor effect. Disease responses following such therapy have ranged from partial to complete and have been observed even in patients who have failed conventional cytokine based strategies. This article reviews the design, methodology and early clinical results of studies investigating the use of allogeneic stem cell transplantation in metastatic renal cell carcinoma.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".