Disease monitoring with quantitative serum IgA levels provides a more reliable response assessment in multiple myeloma patients
Bibliographic record
Abstract
Unlike IgG monoclonal proteins (MCPs), IgA MCP quantification is unreliable due to beta-migration of IgA MCPs on serum protein electrophoresis (SPEP). The utility of nephelometric quantitative IgA (qIgA) to monitor IgA multiple myeloma (MM) is unclear. We retrospectively studied disease response kinetics using qIgA versus MCPs by SPEP, and developed and validated novel qIgA disease assessment criteria in 491 IgA MM patients. The SPEP MCP nadir occurred a median of 41 (IQR 0-102) days before the qIgA. The median time to achieve a partial response (PR) was shorter using standard IMWG versus qIgA response criteria (32 vs 58 days, p < 0.001). Stratification by qIgA criteria, unlike IMWG criteria, led to clear separation of the progression-free survival curves of patients achieving a PR or very good PR. There was a consistent trend toward earlier detection of disease progression using qIgA versus IMWG progression criteria. In conclusion, monitoring IgA MM using MCP-based IMWG criteria may be falsely reassuring, given that MCP levels on SPEP decrease faster than qIgA levels. The qIgA response criteria more accurately stratify patients based on the progression risk and may detect disease progression earlier, which may lead to more consistent measurement of trial endpoints and improved patient outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".