Impact of Declining Fertility Rates in Canada on Donor Options in Blood and Marrow Transplantation
Bibliographic record
Abstract
An HLA-matched sibling remains the optimal donor for most patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT). Marked declines in total fertility rates in Canada over the past 50 years will lead to increasing numbers of patients without sibling donors well into the future. We retrieved transplantation data from a Canadian center and the Canadian Blood and Marrow Transplant Group and total fertility data from the United Nations Department of Economic and Social Affairs. The mean age of adults with acute myelogenous leukemia (AML), who underwent transplantation at The Ottawa Hospital between 1995 and 2004, was 41 +/- 12 years (n = 87). The chance of finding 1 or more HLA-matched sibling donors for a patient with AML treated in 2002 is reflected by the total fertility rate in 1961 (average birth year for patients and sibling donors). The sibling rate for 1961 is the total fertility rate-1.0, or 2.68. The chance of having 1 or more HLA-matched sibling is 53.7% (1-chances of no matched sibling, or 1 - 0.75(2.68)). In 2009, the chance of identifying a matched sibling is only 37.1%, because of declining total fertility rates. Following this trend, this chance will be 24.6% in 2014 and 16.6% in 2024. Greater reliance on alternative donors, such as umbilical cord blood (UCB) and HLA-mismatched donors, can be anticipated. The issue of declining fertility rates appears to be regional, and the impact on transplantation will be more pronounced in Canada than in other developed nations.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".