Prevalence of Hematopoietic Cell Transplant Survivors in the United States
Bibliographic record
Abstract
Advances in hematopoietic cell transplantation (HCT) have led to an increasing number of transplant survivors. To adequately support their healthcare needs, there is a need to know the prevalence of HCT survivors. We used data on 170,628 recipients of autologous and allogeneic HCT reported to the Center for International Blood and Marrow Transplant Research from 1968 to 2009 to estimate the current and future number of HCT survivors in the United States. Stacked cohort simulation models were used to estimate the number of HCT survivors in the United States in 2009 and to make projections for HCT survivors by the year 2030. There were 108,900 (range, 100,500 to 115,200) HCT survivors in the United States in 2009. This included 67,000 autologous HCT and 41,900 allogeneic HCT survivors. The number of HCT survivors is estimated to increase by 2.5 times by the year 2020 (242,000 survivors) and 5 times by the year 2030 (502,000 survivors). By 2030, the age at transplant will be < 18 years for 14% of all survivors (n = 64,000), 18 to 59 years for 61% survivors (n = 276,000), and 60 years and older for 25% of survivors (n = 113,000). In coming decades, a large number of individuals will be HCT survivors. Transplant center providers, hematologists, oncologists, primary care physicians, and other specialty providers will need to be familiar with the unique and complex health issues faced by this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".