Development of a Medicare Health Outcomes Survey Deficit-Accumulation Frailty Index and Its Application to Older Patients With Newly Diagnosed Multiple Myeloma
Bibliographic record
Abstract
Purpose To develop a frailty index using the Rockwood Accumulation of Deficits approach for the Medicare Health Outcomes Survey (MHOS) and apply it in a subset of older patients with newly diagnosed multiple myeloma. Methods Data from 2,692,361 patients without cancer, > 66 years of age, in SEER-MHOS linked databases between 1998 and 2009 were analyzed. A frailty index was constructed, resulting in a 25-item scale; cutoff values were created for individuals classified as frail. This frailty index was then applied to 305 patients with newly diagnosed myeloma in the database to predict overall survival. Results In the derivation cohort of patients without cancer, the median age was 74 years and the mean frailty index was 0.23 (standard deviation, 0.17). Among patients without cancer, each 10% increase in frailty index (approximately three to four more deficits) was associated with a 40% increased risk for death (adjusted hazard ratio, 1.397; 95% CI, 1.396 to 1.399; P < .001). In the cohort of patients with newly diagnosed myeloma, the median age was 76 years and the mean frailty index was 0.28 (standard deviation, 0.17). Each 10% increase in frailty index was associated with a 16% increased risk for death (adjusted hazard ratio, 1.159; 95% CI, 1.080 to 1.244; P < .001). Fifty-three percent of patients with multiple myeloma were considered frail. The estimated median overall survival of patients considered frail was 26.8 months, compared with 43.7 months ( P = .015) for those who were not. Conclusion The MHOS-based frailty index was prognostic for patients with multiple myeloma in predicting overall survival.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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".