Set‐up and routine use of a database of 10 555 genotyped blood donors to facilitate the screening of compatible blood components for alloimmunized patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Large-scale genotyping of blood donors for red blood cell and platelet antigens has been predicted to replace phenotyping assays in the screening of compatible blood components for alloimmunized patients. Although several genotyping platforms have been described, novel procedures and processes are needed to perform genotyping efficiently and to maximize its benefits for blood banks. MATERIALS AND METHODS: Here we describe the processes and procedures developed to introduce large-scale genotyping in our routine operations. RESULTS: Preliminary cost-benefit analysis indicated that genotyping must target frequent blood donors (> 3 donations/year) to be efficiently used. A custom-designed computer application was developed to manage the whole project. It selects frequent donors among recent donations, prints coded labels to identify blood samples sent to the external genotyping laboratory, and stores genotyping results. It can search for donors compatible for any combination of the 22 genotyped antigens as well as consult the current inventory for the presence of the corresponding blood components. The phenotype of recovered components is confirmed by standard serology techniques prior to shipment to hospitals. CONCLUSION: Since October 2007, 10 555 blood donors have been genotyped. The database is used on a regular basis to find compatible blood components with a genotype-phenotype concordance of 99.6%.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".