Participation in the College of American Pathologists Laboratory Accreditation Program Decreases Variability in B-Lymphoblastic Leukemia and Plasma Cell Myeloma Flow Cytometric Minimal Residual Disease Testing: A Follow-up Survey
Bibliographic record
Abstract
CONTEXT.—: Minimal residual disease (MRD) testing by flow cytometry is ubiquitous in hematolymphoid neoplasm monitoring, especially B-lymphoblastic leukemia (B-ALL), for which it provides predictive information and guides management. Major heterogeneity was identified in 2014. Subsequently, new Flow Cytometry Checklist items required documentation of the sensitivity determination method and required lower level of detection (LLOD) inclusion in final reports. This study assesses Laboratory Accreditation Program (LAP) participation and new checklist items' impact on flow cytometry MRD testing. OBJECTIVES.—: To survey flow cytometry laboratories about MRD testing for B-ALL and plasma cell myeloma. In particular, enumerate the laboratories performing MRD testing, the proportion performing assays with very low LLODs, and implementation of new checklist items. DESIGN.—: Supplemental questions were distributed in the 2017-A mailing to 548 flow cytometry laboratories subscribed to the College of American Pathologists FL3 Proficiency Testing Survey (Flow Cytometry-Immunophenotypic Characterization of Leukemia/Lymphoma). RESULTS.—: The percentage of laboratories performing MRD studies has significantly decreased since 2014. Wide ranges of LLOD and collection event numbers were reported for B-ALL and plasma cell myeloma. Most laboratories determine LLOD by using dilutional studies and include it in final reports; a higher proportion of LAP participants used these practices than nonparticipants. CONCLUSIONS.—: Several MRD testing aspects vary among laboratories receiving FL3 Proficiency Testing materials. After the survey in 2014, new checklist items were implemented. As compared to 2014, fewer laboratories are performing MRD studies. While LLOD remains heterogeneous, a high proportion of LAP subscribers follow the new checklist requirements and, overall, target LLOD recommendations from disease-specific working groups are met.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".