A Single‐Center Study on Frontline Treatment for Multiple Myeloma Patients With 1q Abnormalities
Bibliographic record
Abstract
ABSTRACT Introduction Chromosome 1q copy gains (with‐1q‐gain) is a frequently observed genetic abnormality in multiple myeloma (MM) patients. Recent research has demonstrated that 1q gain is a prognostic factor, linked to poorer clinical outcomes. Methods This study was conducted at the Princess Margaret Cancer Centre to examine the clinical outcomes of newly diagnosed MM patients’ with‐1q‐gain or without‐1q‐gain abnormality. The study included 275 patients, with 161 (58.5%) with‐1q‐gain abnormality. The median follow‐up time for the cohort was 94.3 months (95% CI 30.1–38.6). Results The patients’ with‐1q‐gain when compared to without‐1q‐gain were more likely to have other high‐risk cytogenetic abnormalities (34.8% vs. 14.0%, p < 0.001) and more advanced disease according to the International Staging System (ISS III, p < 0.014). Furthermore, a relatively higher proportion of with‐1q‐gain patients received tandem autologous stem cell transplant (ASCT) as frontline therapy (36.2% vs. 8.7%, p ≤ 0.001). To assess the impact of 1q copy number, patients with 3 copies of 1q (1q‐gain3) were compared to those with ≥4 copies (1q‐Amp). No significant differences were observed between the two groups. Conclusion In conclusion, our study provides insight into the clinical significance of 1q gain abnormality in MM patients at a single center, and highlights its association with adverse prognostic features and treatment outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".