Initial flow cytometric evaluation of the Clearllab lymphoid screen
Bibliographic record
Abstract
INTRODUCTION: Flow cytometric immunophenotyping (FCI) is an integral part in the diagnosis and classification of hematologic malignancies. FCI results also influence therapeutic decisions and disease prognosis. ClearLLab LS is a 12-antibody 10-color cocktail provided in dry format designed as a screen for patients suspected of having hematolymphoid disease. METHODS: A blinded comparison between ClearLLab LS, (CD8-FITC, Kappa-FITC,CD4-PE, Lambda-PE, CD19-ECD, CD56-PE-Cy5.5, CD10-PE-Cy7, CD34-APC, CD5-APC-A700, CD20-APC-A750, CD3-PB, and CD45-KrO), ClearLLab Reagents (five-color, 17-antibodies) and individual Laboratory Developed Tests (LDTs), was conducted at four laboratories. Evaluation of ClearLLab LS was performed on 210 specimens, compared to the five-color ClearLLab Reagents (IVD and CE-IVD), and a subset (n = 167) to LDTs. RESULTS: ClearLLab LS showed good agreement to ClearLLab Reagents in detecting the absence (104/104) or presence (106/106) of abnormal populations. Of specimens with abnormal populations the ClearLLab LS agreed with the ClearLLab Reagent for neoplasm maturity assessment (70/70 mature and 36/36 immature). Out of 167 specimens with LDTs results, 86 contained abnormal population(s), ClearLLab LS detected 82 (95.3%) of cases. Of the 4 cases not detected by ClearLLab LS, 3 were plasma cell neoplasms and 1 was a mature T cell malignancy. Eighty-one samples with no hematological malignancy as analyzed by LDT were also negative by ClearLLab LS (100% agreement). ClearLLab LS agreed with LDTs assessment of neoplasms' maturity (55/55 mature and 27/27 immature). CONCLUSION: ClearLLab LS screening tube showed excellent agreement between ClearLLab Reagents and with LDT's. The presence of CD34 and CD10 in the tube allowed the detection of blast populations in several acute leukemias and myeloid neoplasms that were tested. © 2017 International Clinical Cytometry Society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".