Genomic aberrations and immunohistochemical markers as prognostic indicators in multiple myeloma
Bibliographic record
Abstract
As patients with multiple myeloma (MM) have a variable clinical course, predictive markers would help determine the appropriate treatment strategy. Clinical staging is commonly used to predict outcome, but tumour marker expression and the underlying genetic changes are increasingly used to assess the biological aggressiveness of the disease. Recent studies have demonstrated the utility of immunohistochemistry in detecting prognostic markers, including fibroblast growth factor receptor 3, cyclin D1, c-maf and p53, which have been associated with various genetic aberrations, including t(4;14), t(11;14), t(14;16) and del(17p). While t(4;14), t(14;16) and del (17p) have been documented to confer a poor prognosis, t(11;14) appears to be a neutral or even favourable factor in some studies. CD56, CD33, CD20 and CXCR4 are promising surface markers due to their roles in MM progression, but further studies of larger cohorts are necessary to assess their prognostic relevance. In this review, the biological function and clinical relevance of the main prognostic markers in MM is discussed, and also the role of immunohistochemistry in the stratification of patients into appropriate risk categories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".