Bone Marrow Immunohistochemistry and Flow Cytometry in the Diagnosis of Malignant Hematologic Diseases With Emphasis on Lymphomas: A Comparative Retrospective Study
Bibliographic record
Abstract
We aim to evaluate the degree of agreement between immunohistochemistry (IHC) and flow cytometry (FC) in the diagnosis of malignant hematologic diseases, mainly lymphomas. A total of 260 bone marrow biopsies, 255 bone marrow aspirates, and 5 other suspensions of 260 patients used for diagnosis of a hematologic malignancy between 2009 and 2012 with both, IHC and FC, were retrospectively analyzed. Overall there is a substantial degree of agreement (κ=0.69) between IHC and FC. Chronic lymphocytic leukemia/small lymphocytic lymphoma, mature T-cell neoplasms, acute leukemias, and myelodysplastic syndromes had the highest concurrence rates (>80%). In nonconcordant cases, an IHC provided diagnosis in 25.4%, and an FC in 4.6%. Lymphomas were diagnosed by an IHC only in 51% of the cases. Both methods have good concurrence rates and are complementary. An IHC has the advantage of combining markers, morphology, and tissue immunoarchitecture, which is beneficial in the diagnosis of lymphomas. An FC is required in leukemias as it is faster and plays an important role in minimal residual disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".