The Use of Digital Mobile Technology for Rapid Assessment of Treatment Induced Cognitive Deficits in Multiple Myeloma Patients.
Bibliographic record
Abstract
Patients with multiple myeloma may experience subjective changes in baseline cognitive function, which can span across various cognitive domains. Currently, there is limited data about the nature, severity, and temporal evolution of these deficits, as well as their relationship with disease and treatment-related factors. While advances in multiple myeloma treatments continue, few prospective trials have evaluated their impact on cognitive function and quality of life. The objective of this retrospective, cross sectional study is to assess potential cognitive changes in multiple myeloma patients undergoing therapy. This study aims to recruit approximately 60 participants, with 16 presently enrolled. To identify incidence and patterns of cognitive change, patients undergo comprehensive cognitive evaluations such as the International Cognition and Cancer Task Force (ICCTF) battery, National Institute of Health Toolbox (NIB-TB), and the five-minute Montreal Cognitive Assessment screener (MoCA). Data collection and preliminary analysis on 16 participants (median age 72 years, 10/16 on first-line therapy) has revealed global cognitive impairment in 5/16 patients using the ICCTF battery and the NIH-TB, with verbal learning and memory, and executive function being the most affected domains. Future initiatives of this research will focus on expanding study population with the aim to validate preliminary trends in a larger cohort. Larger numbers will allow exploration of correlations between subjective and objective cognitive performance, and the potential differential impact of classes of treatment agents on cognitive function. Results from this study may yield hypotheses for future evaluation and validation in larger, prospective trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".