Use of minimal residual disease assessment in the treatment of chronic lymphocytic leukemia
Bibliographic record
Abstract
Progress in chronic lymphocytic leukemia (CLL) therapies has extended greatly the length and depth of remission, with the goal of treatment advancing towards a cure for some patients. Accordingly, clinical endpoints must evolve to capture these outcomes, and to provide faster access to novel therapies. Minimal residual disease (MRD) is an important endpoint representing more accurately the depth of remission than complete response (CR), and is highly prognostic of progression-free survival (PFS) and overall survival (OS). MRD could be considered a key outcome of clinical trials and, as a surrogate for PFS, could identify the most cost-effective and durable treatment sequencing. MRD testing could also determine which patients would benefit from additional therapy and, accordingly, ascertain when therapy should be stopped earlier, to reduce toxicity and increase treatment-free intervals. Our article discusses possible uses of MRD in the modern era of CLL, including its definition, measurement, and value as a surrogate endpoint in clinical trials, and its potential roles in clinical practice.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".