A comparison of molecular prognostic tests in multiple myeloma
Bibliographic record
Abstract
Triaging multiple myeloma patients for stem cell transplantation relies heavily on the identification of high-risk prognostic factors. Although a variety of molecular cytogenetic methods are widely utilized, it is not clear which, if any, has a greater value than others. In addition, identification of the best test(s) would assist in conserving valuable resources, especially at smaller centers. We conducted a comparative study on the bone marrow aspirates of 74 consecutive patients with Salmon-Durie Stage I, II, or III disease. The majority (59/74) of patients were clinically in advanced stage and planned to undergo therapy. On each patient, image analysis of morphologically selected interphase plasmacytes was utilized to ascertain the DNA ploidy level and karyotype analysis was attempted on metaphases obtained following cell culture. Fluorescence in situ hybridization (FISH) for RB1 and TP53 was performed in all cases with >30% bone marrow plasmacytes. Neither the detection of hypodiploidy or hyperdiploidy by image analysis or the deletion of TP53 by FISH correlated with survival (P = .871 and P = .792, respectively). Deletion of the RB1 locus was observed in 27% of patients tested and tended to significance (P = .270). A karyotype was achieved in 43% of patients. An abnormal karyotype was observed only in Stage III patients and was significantly linked to poor survival by Kaplan-Meier analysis (P = .002). Evaluating a small cohort with a short observation period and using the endpoint of death, highlights those tests most linked to advanced stage disease and identifies those patients most likely to benefit from stem cell transplantation. A cost-effective algorithm is suggested in which cytogenetic analysis is performed only on Stage III patients and FISH for a 13q14 locus alone is performed in those with >30% bone marrow plasmacytes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".