Reducing ethnic disparity in access to high‐quality HLA‐matched cord blood units for transplantation: analysis of the Canadian Blood Services' Cord Blood Bank inventory
Bibliographic record
Abstract
BACKGROUND: Launched in 2013, Canadian Blood Services' Cord Blood Bank (CBS' CBB) has built a high-quality, ethnically diverse cord blood repository that aims to reduce ethnic disparity in accessing suitable units for transplantation. METHODS AND RESULTS: As of December 2016, 2000 units have been banked. The self-reported maternal ethnicity was 58% non-Caucasian. Overall, 26% of units were classified as multi-ethnicity with Caucasian (84%) most frequently observed in combination with Asian, First Nations (predominant indigenous peoples in Canada south of the Arctic Circle), or African ethnicity. Utilization scores that incorporate total nucleated and CD34+ cell counts in the CBS' CBB were associated with greater likelihood of utilization compared with the international inventory of units (p < 0.05). The distribution of utilization scores was similar for Caucasians compared with non-Caucasians (p < 0.05). Using HLA genotypes of cord blood units and their mothers, we determined probable ethnic assignments for each haplotype using HaploStats (National Marrow Donor Program). Significant increases in HLA-match likelihoods are predicted for all ethnicities as the inventory grows to its target of 10,000 units and the gap in HLA-match likelihoods for Caucasian and non-Caucasian patients progressively declines. CONCLUSIONS: The CBS' CBB inventory is predicted to have high HLA-matching likelihoods across a broad spectrum of ethnic groups, improving access to high-quality stem cell products for all patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".