Eliminating The Need for Sequential Confirmation of Response in Multiple Myeloma
Bibliographic record
Abstract
ABSTRACT: Disease response and progression assessment in multiple myeloma is based on various measurements of monoclonal protein (serum and urine protein electrophoresis, serum free light chain, and/or quantitative immunoglobulins). Currently, the International Myeloma Working Group consensus response criteria require 2 sequential assessments of any 1 marker made at any time before confirmation of disease progression and the institution of any new therapy. However, this can be cumbersome in clinical trials. Herein, we hypothesized that if 2 markers meet the progression criteria simultaneously, a repeat of either will not be necessary for confirmation. We retrospectively studied all sequential patients with myeloma enrolled in clinical trials at Mayo Clinic. We identified 583 episodes of confirmed progression in our study. Among the 583 progression episodes, nearly 70% (sensitivity of the simultaneous criteria) met the 2 simultaneous variable criteria at the first testing, indicating progression. Conversely, among 413 patients who met progression criteria by 2 simultaneous values, 98% (specificity of the simultaneous criteria) of patients subsequently had confirmed progression by sequential values. In summary, for patients with 2 disease burden markers meeting the simultaneous progression criteria, sequential assessment of either 1 for confirmation may not be necessary to determine disease progression.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".