Flow cytometry—Recognizing unusual populations in leukemia and lymphoma diagnosis
Bibliographic record
Abstract
Flow cytometry is an invaluable technology in the examination of blood, bone marrow, tissue and body fluids for the presence or absence of hematological disease. It is used in both diagnostic and follow-up testing, with an increasingly important role in the detection of very small residual disease populations (Minimal Residual Disease, MRD) However, flow cytometry immunophenotyping of leukemia and lymphoma is highly dependent on interpretation of results and with the increased complexity of 8-10 color instruments routinely used in clinical laboratories, knowledge of disease-defining populations is increasingly important as is recognizing normal and reactive patterns. This manuscript presents case studies with flow cytometric patterns encountered in routine screening of samples sent for leukemia and lymphoma immunophenotyping, focusing mainly on B-cell disorders which may be missed or incorrectly interpreted by the laboratory (including a hematopathologist) performing the test. Case studies are used to illustrate our laboratory's standardized approach to the interpretation of flow cytometric data. In addition to a standardized approach, these cases emphasize the importance of interpretative skills of technologist and hematopathologists in recognizing abnormal patterns in detecting hematological malignancies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".