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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".