Coombs’ crossmatch after negative antibody screening - a retrospective observational study comparing the tube test and the microcolumn technology
Bibliographic record
Abstract
Background The antiglobulin crossmatch (AHG XM) is mandatory in Germany and Austria for all patients scheduled for red blood cell (RBC) transfusions. We assessed how many biological relevant RBC alloantibodies are identified by the AHG XM in patients with negative antibody screen (ABS), comparing two screening methods: the conventional tube test (CTT) and the microcolumn technology (MCT). Materials and Methods All AHG XMs performed in the Department of Transfusion Medicine, University Greifswald, were retrospectively analysed for an eight year period. Data source included test results for ABS, AHG XMs and antibody identification. The study period consisted of three parts: (1) 2-cell ABS and XM by CTT; (2) 2-cell ABS and XM by MCT; (3) 3-cells ABS and XM by MCT. Results A total of 312 275 AHG XMs were assessed: 105 647 CTT and 206 628 MCT (after 2-cell ABS: 80 295 and after 3-cell ABS 126 333). There was a fivefold increase in reactive AHG XMs using MCT compared to CTT XMs (0·25% vs. 0·05% respectively; P < 0·001). Excluding anti-A1, other RBC alloantibodies were found with a very low frequency regardless of the method used [CTT 5/105 647 (0·005%), MCT 3/80 295 + 2/126 333 (0·002%); P = 0·3]. RBC alloantibodies were identified in only 1% of the reactive MCT XMs. The two RBC alloantibodies identified after 3-cell ABS were of minor clinical relevance (anti-P1, anti-M). Conclusion When an AHG XM becomes reactive after negative ABS result, this is caused with very few exceptions by clinically irrelevant reactivities. This especially accounts for reactive AHG XM using the MCT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".