P53 Gene Deletion Detected by Fluorescence In Situ Hybridization Is an Adverse Prognostic Factor for Patients with Multiple Myeloma Following Autologous Stem Cell Transplantation.
Bibliographic record
Abstract
Abstract We investigated the relevance of p53 deletions to the clinical outcome of multiple myeloma (MM) patients treated with high-dose chemotherapy and autologous stem cell transplantation. Hemizygous p53 gene deletions were detected by cytoplasmic Ig-enhanced interphase fluorescence in situ hybridization (cIg-FISH) in 10 of 105 (9.5%) patients studied. p53 deletions were associated with higher serum calcium (p=0.0062) and creatinine (p=0.013) levels but there were no association with patient age, gender, β-2 microglobulin, C-reactive protein, hemoglobin, albumin, bone lytic lesions, or immunoglobulin isotype. There were no association of p53 deletions with chromosome 13q deletions, translocation t(11;14) or t(4;14). The overall response rates were similar in patients with and without p53 deletions (67% vs 71%). However, patients with p53 deletions had significantly shorter progression free (median 7.9 vs. 25.7 months, p=0.0324) and overall survival (median 14.7 vs. 48.1 months, p=0.0008) than patients without a p53 deletion. A multivariate analysis confirmed p53 deletion was an independent prognostic factor predicting shortened progression free (p=0.0009) or overall survival (p=0.0002) in myeloma patients after high-dose chemotherapy and autologous stem cell transplantation.
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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 | 0.000 | 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) | 0.001 | 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".