Abstract 25 ECTmatch: Optimizing Small-Scale Cord Blood Banking Through HLA Analysis
Bibliographic record
Abstract
Abstract Introduction Cord blood (CB) banks have had to rely on large inventories of CB units to try to serve the largest possible proportion of the population, all the while prioritizing collection of non-Caucasian ethnic groups. However, due to the high linkage disequilibrium of HLA genes and the high frequency of several HLA alleles in the population, CB banks contain hundreds of CB units that could be matched to the same patients, making the inventory somewhat redundant from a clinical standpoint. Objective ExCellThera developed ECTmatch, an algorithm dedicated to optimizing the selection of CB units based on in-depth HLA analysis in order to maximize the efficiency of the bank to suitably match the largest proportion of subjects within a small pool of donors. Methods The performance of ECTmatch was evaluated in a simulation aiming to select 100 CB units from the Héma-Québec CB bank that satisfied an arbitrary minimal cell content criteria of 120 × 107 TNC and 6 × 106 CD34+ cells (n = 2,987). Selection was performed to optimize matching for the Quebec population, with a minimal HLA-match of 5/8 for HLA-A, -B, -C, and -DRB1. Results ECTmatch provides a suitably matched donor for 71.5% of the Quebec population, compared with only 45.0% (±2.4%) with random selection. Because patients who require a CB transplant tend to have rarer HLAs, the performance of ECTmatch was evaluated for this specific subset of patients (n = 62). Again, ECTmatch outperformed random selection, by providing a donor for 54.8% of patients, compared with only 29.9% with random selection. Finally, while ECTmatch was developed to optimize CB selection specifically for the Quebec population, it still outperformed random selection for subjects from the other Canadian provinces or the USA. Discussion By selecting CB units based on HLA profiles, ECTmatch allows the creation of a highly useful inventory with a very low number of CB units. This approach to small-scale CB banking can be adapted to different population subsets and could be used to select a subset of CB units for pre-release for immediate clinical availability or for the creation of a pre-expanded CB inventory with maximal population coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".