A 10-Item Frailty Index Based on a Comprehensive Geriatric Assessment (FI-CGA-10) in Older Adults with Cancer: Development and Construct Validation
Bibliographic record
Abstract
BACKGROUND: A frailty index (FI) based on domain-level deficits identified through a comprehensive geriatric assessment (CGA) has been previously developed and validated in general geriatric patients. Our objectives were to construct an FI-CGA and to assess its construct validity in the geriatric oncology setting. METHODS: Five hundred forty consecutive Japanese patients with cancer who underwent a CGA on a geriatric oncology service were included (median age 80 years, range 66-96 years). We developed a 10-item frailty index based on deficits in 10 domains (FI-CGA-10): cognition, mood, communication, mobility, balance, nutrition, basic and instrumental activities of daily living, social support, and comorbidity. Deficits in each domain were scored as 0 (no problem), 0.5 (minor problem), and 1.0 (major problem). Scores were calculated by dividing the sum of the scores for each domain by 10 and then categorized as fit (<0.2), pre-frail (0.2-0.35), and frail (>0.35). Construct validity was tested by correlating the FI-CGA-10 with other established frailty measures. RESULTS: FI-CGA-10 was well approximated by the gamma distribution. Overall, 20% of patients were fit, 41% were pre-frail, and 39% were frail. FI-CGA-10 was correlated with Canadian Study of Health and Aging (CSHA) Clinical Frailty Scale (r = 0.83), CSHA rules-based frailty definition (r = 0.67), and CSHA Function Score (r = 0.77). Increasing levels of frailty were significantly associated with functional and cognitive impairments, high comorbidity burden, poor self-rated health, and low estimated survival probabilities. CONCLUSION: The FI-CGA-10 is a user-friendly and construct-validated measure for quantifying frailty from a CGA. IMPLICATIONS FOR PRACTICE: This article describes the construction of a user-friendly 10-item frailty index based on a comprehensive geriatric assessment (FI-CGA-10) for older adults with cancer: cognition, mood, communication, mobility, balance, nutrition, basic and instrumental activities of daily living, social support, and comorbidity. The FI-CGA-10 simplifies the original FI-CGA used in the general geriatric setting while maintaining its content validity. The index's construct validity was demonstrated in a cohort of older adults with various cancer types. The advantage of the FI-CGA-10 is that a frailty score can be calculated more readily and interpreted in a more clinically sensible manner than the original FI-CGA.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".