White Blood Cell Enumeration and Differential by Flow Cytometry: The <scp>ICSH WBC</scp> Reference Method
Bibliographic record
Abstract
INTRODUCTION: The current reference method for the white blood cell (WBC) differential is manual smear review as outlined in CLSI H20-A2. As with many manual methods, it suffers from a number of challenges including dependence upon the expertise of the interpreter, the quality of the smear and stain, when dysplastic features make cell identification difficult, imprecision with leucopenia, and enumeration bias due to non-uniform cell distribution. METHODS: This study describes an alternative method for establishing the leucocyte differential using a single-tube, 8-color flow cytometric reference method. RESULTS: Data presented is from an international comparison of normal (based on analyzer counts, N = 120) and abnormal (N = 496) clinical samples performed at four institutions using four different models of flow cytometers. Here we demonstrate equivalent performance between the flow cytometric method and the current manual reference method, but show improved performance of the proposed reference method for low/infrequent cell populations, for example, monocytes and basophils. CONCLUSION: The flow cytometric method also performs well in comparison with hematology analyzers in current clinical use, including good correlation for total white blood cell enumeration. The findings indicate that the flow cytometric method, deemed the "ICSH WBC reference," could be used in lieu of CLSI H20-A2 as a reference for white blood cell enumeration and differential counting and specifically for the evaluation of automated differential counters.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".