A ten‐color tube with dried antibody reagents for the screening of hematological malignancies
Bibliographic record
Abstract
INTRODUCTION: The workflow in clinical flow cytometry laboratories must constantly be reviewed to develop technical procedures that improve quality and productivity and reduce costs. Using the Beckman Coulter dry coating technology, we customized a ten-color tube with dried antibody reagents, designated the Duraclone screening tube (DST), for screening hematological malignancies. Here, we compared the applicability, clinical and numerical equivalence, and cost and time required for the technical procedures between the liquid reagents and the DST. METHODS: The DST contains CD4 + Kappa-FITC, CD8 + Lambda-PE, CD3 + CD14-ECD, CD33-PE-Cy5.5, CD20 + CD56-PE-Cy7, CD34-APC, CD19-APC-AlexaFluor700, CD10-APC-AlexaFluor750, CD5-Pacific Blue, and CD45-Krome Orange. We evaluated 20 bone marrow samples, 13 peripheral blood samples, 6 lymph node biopsy samples, 5 fine-needle aspirate samples, 5 cerebrospinal fluid samples, and 1 pleural fluid sample. RESULTS: The DST was useful for more than 60% of our samples. It was able to enumerate the majority of the populations in all types of samples with a statistically acceptable correlation with the liquid reagents. The use of the DST translated into significant time and cost savings of 15.8% and 12.3%, respectively, compared with the use of the liquid reagent. The cost was reduced by $14.36 per sample. CONCLUSIONS: The DST is an efficient solution for screening hematological malignancies with improved quality, productivity, standardization, and sustainability. These improvements could benefit patients by providing faster diagnoses using a higher quality and lower cost reagent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".