<scp>HLA</scp>‐haplotype redundancy and rareness in Canadian Blood Services' Stem Cell Registry and Cord Blood Bank: Novel metrics for optimizing utility
Bibliographic record
Abstract
BACKGROUND: The utility of unrelated donor registries that support allogeneic hematopoietic cell transplantation could be optimized through greater understanding of redundancy and rareness of HLA phenotypes. METHODS: HLA phenotype rareness was determined using known HLA haplotype frequencies. Donor redundancy was determined through pairwise comparison of donor HLA profiles within an inventory. RESULTS: Among 61,730 registrants in the Canadian Blood Services (CBS) Stem Cell Registry (SCR) with high resolution HLA typing at 5 loci, 6.6% of HLA phenotypes were redundant with variation across ethnic groups (8.3% of Caucasian phenotypes; 8% of Native American/First Nations, 4.4% of Asia-Pacific Islanders (API), 2.1% of Hispanic, 0.7% of African-American (AFA), and 4.5% of other ethnicities). A total of 18.5% of registrants had redundant HLA phenotypes with variation across ethnic groups. All 3716 cord blood units in the CBS's cord blood bank (CBB) had high resolution HLA typing at 5 loci and 202 units were redundant (5.4%) comprising 78 HLA phenotypes, with varying rareness. Repeated HLA phenotypes were from Caucasian donors (77%), multiple ethnicity (13%), API (9%), and AFA (1%). Registrants and CBUs with AFA ethnicity had the rarest phenotypes while Caucasian ethnicity was associated with the most common HLA phenotypes. CONCLUSIONS: Redundancy was greater in the SCR compared to the CBB and was most common with CAU ethnicity. Recruiting non-Caucasian registrants and continued cord blood banking should reduce redundancy. A sub-inventory of redundant donors and cord blood units could support new uses for donor-supported cellular therapies that do not require HLA matching.
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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".