Comparison of five platforms for enumeration of residual leucocytes in leucoreduced blood components
Bibliographic record
Abstract
The need for quality control of leucoreduction of blood products has led to the development of various methods to count low levels of residual leucocytes. We compared five platforms side-by-side: the Nageotte haemocytometer and four based on fluorescent staining of nuclei: two flowcytometers (Beckman Coulter, BD Biosciences) with methods based on counting beads, a volumetric flow cytometer (Partec) and the microvolumic fluorimeter ImagN2000 (BD Biosciences), all according to their manufacturers' recommended methods. Analysis of double-filtered red cell concentrates (RCCs) and platelet concentrates (PCs), spiked with various numbers of leucocytes, revealed good linearity for all methods over the range of 1.6-32.7 leucocytes/microl, all with r(2) > 0.99. At the rejection level of leucocyte-reduced blood components, i.e. 1 x 10(6) per unit corresponding with approximately 3.3 leucocytes/microl, the Nageotte haemocytometer had low accuracy (0% for RCCs, 56% for PCs), and was relatively imprecise [coefficient of variance (CV) of 34% and 30% respectively]. The Partec flow cytometer gave good results for RCCs (accuracy 67%, CV 22%), but not for PCs (accuracy 0%, CV 25%). The ImagN2000 had an accuracy of 44% for RCCs and 89% for PCs, but the precision was variable (CV 32% for RCCs, 15% for PCs). The best results were obtained with the Beckman Coulter (RCCs: accuracy 86%, CV 13%, PCs: accuracy 67%, CV 16%), and BD Biosciences platforms (RCCs: accuracy 100%, CV 10%; PCs: accuracy 89%, CV 11%). We conclude that, at the rejection level of 1 x 10(6) leucocytes per unit, the widely used Nageotte haemocytometer performs poorly in terms of inaccuracy and imprecision, and that both counting-bead-based, flow cytometric methods performed best.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".