Immunophenotyping of selected hematologic disorders – focus on lymphoproliferative disorders with more than one malignant cell population
Bibliographic record
Abstract
Currently, clinical laboratories face increasing demand for flow cytometry testing combined with limited funding. Therefore, many laboratories search for panels that would provide sufficient immunophenotyping information and meet economical requirements. At the Flow Cytometry Laboratory, University Health Network, Toronto, ON, Canada, we apply two 10-color tubes of surface markers for diagnosis of lymphoproliferative disorders (LPDs). These tubes contain most of the mandatory B- and T-cell markers according to European Leukemia Net (www.leukemia-net.org) recommendations. The B-cell-oriented panel includes the following antibodies: Kappa-FITC/lambda-PE/CD19-ECD/CD38-PC5.5/CD20-PC7/CD34-APC/CD23 APC-AF700/CD10 APC-AF750/CD5-PB/CD45-KO. A different combination is applied to detect cytoplasmic Ig light chain expression and aberrant immunophenotype of plasma cells. The T-cell panel allows enumeration of various T- and NK-cell subsets: CD57-FITC/CD11c-PE/CD8-ECD/CD3-PC5.5/CD2-PC7/CD56-APC/CD7-APC-AF700/CD4-APC-AF750/CD5-PB/CD45-KO. The reported overall incidence of B-cell chronic LPDs presenting with more than one aberrant population is approximately 5%. Multicolor analysis facilitates the detection of multiple aberrant populations in the same sample because expression of multiple antigens can be studied simultaneously in each defined population. Examples of LPDs with multiple aberrant populations are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".