Dynamic frailty risk assessment among older adults with multiple myeloma: A population-based cohort study
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Bibliographic record
Abstract
Multiple myeloma (MM) is a cancer of older adults and those who are more frail are at high risk of poor outcomes. Current tools for identifying and categorizing frail patients are often static and measured only at the time of diagnosis. The concept of dynamic frailty (i.e. frailty changing over time) is largely unexplored in MM. In our study, adults with newly-diagnosed MM who received novel drugs between the years 2007-2014 were identified in the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked databases. Using a previously published cumulative deficit approach, a frailty index score was calculated at diagnosis and each landmark interval (1-yr, 2-yr, 3-yr post diagnosis). The association of frailty with overall survival (OS) both at baseline and at each landmark interval as well as factors associated with worsening frailty status over time were evaluated. Overall, 4617 patients were included. At baseline, 39% of the patients were categorized as moderately frail or severely frail. Among those who had 3 years of follow-up, frailty categorization changed post diagnosis in 93% of the cohort (78% improved and 72% deteriorated at least at one time point during the follow up period). In a landmark analysis, the predictive ability of frailty at the time of diagnosis decreased over time for OS (Harrell's C Statistic 0.65 at diagnosis, 0.63 at 1-yr, 0.62 at 2-yr, and 0.60 at 3-yr) and was inferior compared to current frailty status at each landmark interval. Our study is one of the first to demonstrate the dynamic nature of frailty among older adults with MM. Frailty may improve or deteriorate over time. Current frailty status is a better predictor of outcomes than frailty status at time of diagnosis, indicating the need for re-measurement in this high-risk patient population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it