Trends in Use of and Survival after Autologous Hematopoietic Cell Transplantation in North America, 1995-2005: Significant Improvement in Survival for Lymphoma and Myeloma during a Period of Increasing Recipient Age
Bibliographic record
Abstract
Autologous hematopoietic cell transplantation (auto-HCT) is performed to treat relapsed and recurrent malignant disorders and as part of initial therapy for selected malignancies. This study evaluated changes in use, techniques, and survival in a population-based cohort of 68,404 patients who underwent first auto-HCT in a US or Canadian center between 1994 and 2005 and were reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). The mean annual number of auto-HCTs performed was highest during 1996-1999 (6948), and decreased subsequently 2000-2003 (4783), owing mainly to fewer auto-HCTs done to treat breast cancer. However, the mean annual number of auto-HCTs increased from 5278 annually in 1994-1995 to 5459 annually in 2004-2005, reflecting increased use for multiple myeloma, non-Hodgkin lymphoma, and Hodgkin lymphoma. Despite an increase in the median recipient age from 44 to 53 years, there has been a significant improvement in overall survival (OS) from 1994 to 2005 in patients with chemotherapy-sensitive relapsed non-Hodgkin lymphoma (day +100 OS, from 85% to 96%; 1-year OS, from 68% to 80%; P < .001) and chemotherapy-sensitive multiple myeloma (day +100 OS, from 96% to 98%; 1-year OS, from 83% to 92%; P < .001). This improvement in OS was most pronounced in middle-aged (>40 years) and older (>60 years) individuals.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".