A QA Program for MRD Testing Demonstrates That Systematic Education Can Reduce Discordance Among Experienced Interpreters
Bibliographic record
Abstract
BACKGROUND: Minimal residual disease (MRD) in B lymphoblastic leukemia (B-ALL) by flow cytometry is an established prognostic factor used to adjust treatment in most pediatric therapeutic protocols. MRD in B-ALL has been standardized by the Children's Oncology Group (COG) in North America, but not routine clinical labs. The Foundation for National Institutes of Health sought to harmonize MRD measurement among COG, oncology groups, academic, community and government, laboratories. METHODS: Listmode data from post-induction marrows were distributed from a reference lab to seven different clinical FCM labs with variable experience in B-ALL MRD. Labs were provided with the COG protocol. Files from 15 cases were distributed to the seven labs. Educational sessions were implemented, and 10 more listmode file cases analyzed. RESULTS: Among 105 initial challenges, the overall discordance rate was 26%. In the final round, performance improved considerably; out of 70 challenges, there were five false positives and one false negative (9% discordance), and no quantitative discordance. Four of six deviations occurred in a single lab. Three samples with hematogones were still misclassified as MRD. CONCLUSIONS: Despite the provision of the COG standardized analysis protocol, even experienced laboratories require an educational component for B-ALL MRD analysis by FCM. Recognition of hematogones remains challenging for some labs when using the COG protocol. The results from this study suggest that dissemination of MRD testing to other North American laboratories as part of routine clinical management of B-ALL is possible but requires additional educational components to complement standardized methodology. © 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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".