Flow cytometric testing for paroxysmal nocturnal hemoglobinuria: CD64 is better for gating monocytes than CD33
Bibliographic record
Abstract
BACKGROUND: Paroxysmal nocturnal hemoglobinuria (PNH) is diagnosed by documenting partial or complete absence of glycosyl phosphatidyl inositol (GPI)-associated ligands in neutrophils, monocytes, and red blood cells (RBCs). The monocytes can be separated by their bright expression of either CD33 or CD64. This paper compares the utility of CD33- vs CD64-based monocyte gating in flow cytometric testing for PNH. METHODS: One hundred and nineteen cases tested for PNH by flow cytometry were included in the study. Both the total number of monocytes and the number of GPI-deficient monocytes gated with CD33 or CD64 were compared. The clustering pattern and any other unusual patterns were noted and investigated. RESULTS: CD64 staining showed more distinct separation of the monocyte cluster than did CD33 staining. The difference between the number of monocytes gated by CD33 and CD64 staining ranged from -26 to +32% (median 1.60%, average 1.69%). Six patients had GPI-deficient monocytes by both CD33- and CD64-based gating, ranging from 0.02 to 83.23%. There were no patients who showed GPI-deficient monocytes by one but not the other gating. The presence of blasts in patients with acute leukemia resulted in abnormal cluster patterns, both by CD33- and CD64-based gating. CONCLUSIONS: CD64-based gating showed more distinct clustering of monocytes than CD33-based gating, allowing for objective separation. The number of monocytes in total and GPI-deficient monocytes derived from both gating strategies was comparable.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".