Proceedings from the Second Haploidentical Stem Cell Transplantation Symposium—Haplo2014, San Francisco, California, December 4, 2014
Bibliographic record
Abstract
Significant progress has been made over the past decade in haploidentical transplantation, with the development of novel methods to control intense alloreactive reactions generated in the major HLA-mismatched setting. Application of post-transplantation cyclophosphamide has gained worldwide acceptance as an effective and low-cost way to perform this type of transplantation, with outcomes now similar to those from HLA-matched unrelated donors. These advances have resulted in improved treatment-related mortality, whereas disease relapse has emerged as the most common cause of treatment failure. In addition, improvements in immunologic reconstitution after transplantation are much needed, not only in haploidentical transplantation but in all forms of stem cell transplantation. This symposium has focused on some of the most promising methods to control alloreactivity in this form of transplantation and application of cellular therapy to prevent disease relapse after transplantation, as well as understanding immunologic reconstitution and foreseeable approaches to improve immune recovery after transplantation.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".