Will Post-Transplantation Cell Therapies for Pediatric Patients Become Standard of Care?
Bibliographic record
Abstract
Although allogeneic hematopoietic stem cell transplantation (HSCT) is a curative approach for many pediatric patients with hematologic malignancies and some nonmalignant disorders, some critical obstacles remain to be overcome, including relapse, engraftment failure, graft-versus-host disease (GVHD), and infection. Harnessing the immune system to induce a graft-versus-tumor effect or rapidly restore antiviral immunity through the use of donor lymphocyte infusion (DLI) has been remarkably successful in some settings. Unfortunately, however, the responses to DLI can be variable, and GVHD is common. Thus, manipulations to minimize GVHD while restoring antiviral immunity and enhancing the graft-versus-tumor effect are needed to improve outcomes after allogeneic HSCT. Cellular therapies, defined as treatment modalities in which hematopoietic or nonhematopoietic cells are used as therapeutic agents, offer this promise for improving outcomes post-HSCT. This review presents an overview of the field for pediatric cell therapies in the transplant setting and discusses how we can broaden applicability beyond phase I.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".