Improved access to better <scp>HLA</scp>‐matched hematopoietic cells for allogeneic transplant: analysis of donors and cord blood units selected for Canadian patients in 2018
Bibliographic record
Abstract
BACKGROUND: Patients undergoing hematopoietic cell transplantation (HCT) often require use of an unrelated donor or cord blood unit (CBU). An understanding of evolving practices in graft selection is needed for optimization of donor recruitment and cord blood collection. STUDY DESIGN AND METHODS: Each donor workup (WU) requested in 2018 involving a Canadian (CDN) patient and unique donor product or CBU was reviewed (n = 598). Degree of HLA match; product origin (domestic or international [INT]); and non-HLA factors including donor age, sex, cytomegalovirus (CMV), and ABO compatibility were analyzed for WUs that proceeded to transplant (n = 414). We also analyzed changes compared to a similar analysis performed in 2013. RESULTS: The majority of transplants used matched unrelated donors (MUDs; n = 323; 78%) and were most often young (≤35 years), male, INT donors (n = 136). The proportion of transplants involving MUDs, as opposed to mismatched unrelated donors or CBUs, increased by 12.4% compared with 2013. When young, male, CDN MUDs were identified in patient search reports but not selected, CMV mismatching and ABO incompatibility were most likely to have influenced the decision to use an INT MUD. Consistent with global trends, CBU transplants decreased compared to 2013; however, the degree of HLA matching improved significantly, and 27% of transplanted CBUs were procured from the Canadian Blood Services Cord Blood Bank. CONCLUSIONS: Access to MUDs and better HLA-matched CBUs by CDN patients has increased since 2013. Ongoing recruitment of young registrants and cord blood donors with diverse HLA haplotypes will support selection of donors with optimal non-HLA characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".