Validation of a flow‐<scp>cytometry‐based</scp> red blood cell antigen phenotyping method
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Current manual and automated phenotyping methods are based on visual detection of the antigen-antibody interaction. This approach has several limitations including the use of large volumes of patient and reagent red blood cells (RBCs) and antisera to produce a visually detectable reaction. We sought to determine whether the flow cytometry could be developed and validated to perform RBC phenotyping to enable a high-throughput method of phenotyping using comparatively miniscule reagent volumes via fluorescence-based detection of antibody binding. MATERIALS AND METHODS: , -S, -s that are commercially available and currently utilized in our blood transfusion services (BTS) for agglutination-based phenotyping assays. RESULTS: Seventy samples were tested using both flow-cytometry-based-phenotyping and a manual tube standard agglutination assay. For all the antigens tested, 100% concordance was achieved. The flow-cytometry-based method used minimal reagent volume (0.5-1 μl per antigen) compared with the volumes required for manual tube standard agglutination (50 μl per antigen) CONCLUSION: This study demonstrates the successful validation of flow-cytometry-based RBC phenotyping. Flow cytometry offers many benefits compared to common conventional RBC phenotyping methods including high degrees of automation, quantitative assessment with automated interpretation of results and extremely low volumes of reagents. This method could be used for high-throughput, low-cost phenotyping for both blood suppliers and hospital BTS.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".