Significant Improvement in Survival After Allogeneic Hematopoietic Cell Transplantation During a Period of Significantly Increased Use, Older Recipient Age, and Use of Unrelated Donors
Bibliographic record
Abstract
PURPOSE: Over the past four decades, allogeneic hematopoietic cell transplantation (alloHCT) has evolved as a curative modality for patients with hematologic diseases. This study describes changes in use, technique, and survival in a population-based cohort. PATIENTS AND METHODS: The study included 38,060 patients with hematologic malignancies or disorders who underwent first alloHCT in a US or Canadian center from 1994 to 2005 and were reported to the Center for International Blood and Marrow Transplant Research. RESULTS: AlloHCT as treatment for acute lymphoblastic (ALL) and myeloid leukemias (AML), myelodysplastic syndrome (MDS), and Hodgkin and non-Hodgkin lymphomas increased by 45%, from 2,520 to 3,668 patients annually. From 1994 to 2005, use of both peripheral (7% to 63%) [corrected] and cord blood increased (2% to 10%), whereas use of marrow decreased (90% to 27%). Despite a median age increase from 33 to 40 years and 165% [corrected] increase in unrelated donors for alloHCT, overall survival (OS) at day 100 significantly improved for patients with AML in first complete remission after myeloablative sibling alloHCT (85% to 94%; P < .001) and unrelated alloHCT (63% to 86%; P < .001); 1-year OS improved among those undergoing unrelated alloHCT (48% to 63%; P = .003) but not among those undergoing sibling alloHCT. Similar results were seen for ALL and MDS. Day-100 OS after cord blood alloHCT improved significantly from 60% to 78% (P < .001) for AML, ALL, MDS, and chronic myeloid leukemia. Use of reduced-intensity regimens increased, yielding OS rates similar to those of myeloablative regimens. CONCLUSION: Survival for those undergoing alloHCT has significantly improved over time. However, new approaches are needed to further improve 1-year OS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".