Design and Analytic Validation of BCR-ABL1 Quantitative Reverse Transcription Polymerase Chain Reaction Assay for Monitoring Minimal Residual Disease
Bibliographic record
Abstract
CONTEXT: Monitoring minimal residual disease by quantitative reverse transcription polymerase chain reaction has proven clinically useful, but as yet there are no Food and Drug Administration-approved tests. Guidelines have been published that provide important information on validation of such tests; however, no practical examples have previously been published. OBJECTIVE: To provide an example of the design and validation of a quantitative reverse transcription polymerase chain reaction test. DESIGN: To describe the approach used by an individual laboratory for development and validation of a laboratory-developed quantitative reverse transcription polymerase chain reaction test for BCR-ABL1 fusion transcripts. RESULTS: Elements of design and analytic validation of a laboratory-developed quantitative molecular test are discussed using quantitative detection of BCR-ABL1 fusion transcripts as an example. CONCLUSIONS: Validation of laboratory-developed quantitative molecular tests requires careful planning and execution to adequately address all required analytic performance parameters. How these are addressed depends on the potential for technical errors and confidence required for a given test result. We demonstrate how one laboratory validated and clinically implemented a quantitative BCR-ABL1 assay that can be used for the management of patients with chronic myelogenous leukemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".