Children’s Oncology Group’s 2023 Blueprint for Research: Cellular Therapy and Stem Cell Transplantation
Bibliographic record
Abstract
Since the publication of the last Cellular Therapy and Stem Cell Transplant blueprint in 2013, Children’s Oncology Group cellular therapy-based trials for advanced the field and created new standards of care across a wide spectrum of pediatric cancer diagnoses. Key findings include that tandem autologous transplant improved survival for patients with neuroblastoma and atypical teratoid/rhabdoid brain tumors, one umbilical cord blood (UCB) donor was safer than two UCB donors, killer immunoglobulin receptor (KIR) mismatched donors did not improve survival for pediatric acute myeloid leukemia when in vivo T cell depletion is used and the depth of remission as measured by next-generation sequencing based minimal residual disease assessment pre-transplant was the best predictor of relapse for acute lymphoblastic leukemia. Plans for the next decade include optimizing donor selection for transplants for acute leukemia/myelodysplastic syndrome, using novel engineered cellular therapies to target a wide array of malignancies, and developing better treatments for cellular therapy toxicities such as viral infections and graft-vs-host disease.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.104 | 0.058 |
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".