Proceedings From the Fourth Haploidentical Stem Cell Transplantation Symposium (HAPLO2016), San Diego, California, December 1, 2016
Bibliographic record
Abstract
The resurgence of haploidentical stem cell transplantation (HaploSCT) over the last decade is one of the most important advances in the field of hematopoietic stem cell transplantation (HSCT). The modified platforms of T cell depletion either ex vivo (CD34 + cell selection, "megadoses" of purified CD34 + cells, or selective depletion of T cells) or newer platforms of in vivo depletion of T cells, with either post-transplantation high-dose cyclophosphamide or intensified immune suppression, have contributed to better outcomes, with survival similar to that in HLA-matched donor transplantation. Further efforts are underway to control viral reactivation using modified T cells, improve immunologic reconstitution, and decrease the relapse rate post-transplantation using donor-derived cellular therapy products, such as genetically modified donor lymphocytes and natural killer cells. Improvements in treatment-related mortality have allowed the extension of haploidentical donor transplants to patients with hemoglobinopathies, such as thalassemia and sickle cell disease, and the possible development of platforms for immunotherapy in solid tumors. Moreover, combining HSCT from a related donor with solid organ transplantation could allow early tapering of immunosuppression in recipients of solid organ transplants and hopefully prevent organ rejection in this setting. This symposium summarizes some of the most important recent advances in HaploSCT and provides a glimpse in the future of fast growing field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".