Development and clinical application of nucleated red blood cell counting and staging on the automated haematology analyser XE-2100<sup>TM</sup>
Bibliographic record
Abstract
We initially developed a new flow cytometric (FCM) reference method for the enumeration and staging of nucleated red blood cells (NRBC) in 1997 [Wang et al., 1998 (XIth International Symposium on Technological Innovations in Laboratory Haematology, Banff, Canada, 1998); Tsuji et al., 1999 (Cytometry, 1999)]. The method used CD45 antibody and propidium iodide staining to separate NRBCs from other cells. Accuracy and precision were enhanced because larger numbers of cells were counted than was possible with the manual method. We also developed a method for automated NRBC counting on a haematology analyser, the XE-2100 (Wang, 1988). NRBC were separated from other cells using a special lysing buffer and a fluorescent dye. The XE-2100 was found to detect peripheral and cord blood NRBC accurately and precisely when compared with cell morphology or FCM control methods. The FCM NRBC staging method was established through the identification of different NRBC populations following the novel staining and lysing method. To evaluate the method further, we sorted samples containing NRBCs using a FACSort and investigated NRBC staging on the Sysmex XE-2100 based on the cell sorting results. Data were analysed using special software (ida). First, we used the data in various parameter combinations. We then established gates to classify the NRBC populations. Finally, we analysed blood specimens from patients with different types of diseases to explore possible clinical applications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".