Cognitive Performance in Patients With Multiple Myeloma Treated With an Autologous Stem Cell Transplant: Results of the Brilliant Study
Bibliographic record
Abstract
BACKGROUND: Patients with newly diagnosed multiple myeloma undergo induction chemotherapy, followed by high-dose chemotherapy and autologous hematopoietic cell transplant (auto-HCT). The impact of auto-HCT on cognitive function is not well understood. We evaluated longitudinal changes in cognitive function after auto-HCT in patients with multiple myeloma. PATIENTS AND METHODS: Patients with newly diagnosed multiple myeloma who completed induction chemotherapy and underwent pre-transplant evaluation were enrolled prospectively. Patients completed the self-administered gerocognitive exam (SAGE) and specialist-administered Montreal cognitive assessment (MoCA) at 3 time points: pre-transplant (baseline), 100-days post-transplant, and 1-year post-transplant. Scores were reported as means and standard deviations. Paired t-tests evaluated longitudinal differences. RESULTS: Thirty-eight (100%) participants completed the MoCA and SAGE at baseline and 100-days post-transplant, and nineteen (50%) participants completed both at 1-year post-transplant. The mean total MoCA score was 25.00 (SD = 3.05) at baseline, 25.87 (SD = 2.68) at 100-days post-transplant, and 26.00 (SD = 2.81) at 1-year post-transplant. The mean total SAGE score was 17.00 (SD = 3.89) at baseline, 17.76 (SD = 3.07) at 100-days post-transplant, and 18.18 (SD = 3.23) at 1-year post-transplant. At 100-days post-transplant, the mean change from baseline in MoCA score was 0.87 (SD = 3.16; P = .10) and the mean change in SAGE score was 0.76 (SD = 2.62; P = .08). Participants did not indicate a preference for self-administered versus provider-administered measures. CONCLUSION: Our study shows that auto-HCT was not associated with cognitive decline on objective assessment. Cognitive assessment is feasible and acceptable, with good correlation between self-administered tests and those administered by trained specialists.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".