Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report
Bibliographic record
Abstract
We conducted a pooled analysis of 869 individual newly diagnosed elderly patient data from 3 prospective trials. At diagnosis, a geriatric assessment had been performed. An additive scoring system (range 0-5), based on age, comorbidities, and cognitive and physical conditions, was developed to identify 3 groups: fit (score = 0, 39%), intermediate fitness (score = 1, 31%), and frail (score ≥2, 30%). The 3-year overall survival was 84% in fit, 76% in intermediate-fitness (hazard ratio [HR], 1.61; P = .042), and 57% in frail (HR, 3.57; P < .001) patients. The cumulative incidence of grade ≥3 nonhematologic adverse events at 12 months was 22.2% in fit, 26.4% in intermediate-fitness (HR, 1.23; P = .217), and 34.0% in frail (HR, 1.74; P < .001) patients. The cumulative incidence of treatment discontinuation at 12 months was 16.5% in fit, 20.8% in intermediate-fitness (HR, 1.41; P = .052), and 31.2% in frail (HR, 2.21; P < .001) patients. Our frailty score predicts mortality and the risk of toxicity in elderly myeloma patients. The International Myeloma Working group proposes this score for the measurement of frailty in designing future clinical trials. These trials are registered at www.clinicaltrials.gov as #NCT01093136 (EMN01), #NCT01190787 (26866138MMY2069), and #NCT01346787 (IST-CAR-506).
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".