Development of a new clinical index to easily assess frailty of elderly patients with multiple myeloma in Asian population
Bibliographic record
Abstract
The number of elderly people is rapidly growing, and the proportion of elderly patients with multiple myeloma (MM) continues to increase. This study aimed to develop a frailty assessment tool based on clinical data and to estimate its feasibility in elderly patients with MM. This study analyzed data from 728 elderly transplant-ineligible patients with newly diagnosed MM who were treated between January 2010 and October 2019. Our clinical frailty index included age (< 75, and ≥ 75 years), Charlson comorbidity index (CCI; < 3 and ≥ 3), and Eastern Cooperative Oncology Group performance status score (ECOG score; 0, 1-2, and ≥ 3). Patients were classified as fit, intermediate, or frail if they had a score of 0, 1, or ≥ 2, respectively. The overall survival rates differed significantly according to frailty (fit vs. intermediate: hazard ratio [HR] = 2.41; 95% confidence interval [CI] = 1.43-4.06; P = 0.001; fit vs. frail: HR = 4.61; 95% CI = 2.74-7.77; P < 0.001 and intermediate vs. frail: HR = 1.91, 95% CI = 1.49-2.45, P < 0.001, respectively). The frail had significantly shorter EFS compared with the fit and intermediate group in our frailty index (fit vs. intermediate: HR = 1.34, 95% CI = 0.92-1.96, P = 0.132; fit vs. frail: HR = 2.06, 95% CI = 1.40-3.02, P < 0.001; and intermediate vs. frail: HR = 1.53, 95% CI = 1.22-1.92, P < 0.001, respectively). The new clinical frailty index, which is based on age, CCI, and ECOG PS, can easily assess frailty in elderly patients with MM and can be helpful in predicting survival outcomes in real world clinical setting.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".