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
e24011 Background: A frailty index based on domain-level deficits in a comprehensive geriatric assessment (FI-CGA) has been previously developed and validated in general geriatric patients (Jones D, Aging Clin Exp Res 2005). Our objectives were to construct an FI-CGA and to assess its construct validity in geriatric oncology setting. Methods: Consecutive older adults with cancer who underwent a CGA on a geriatric oncology service were included. 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 score of each domain by 10, and categorized as fit ( < 0.2), pre-frail (0.2–0.35), and frail ( > 0.35). Construct validity was tested by comparing the FI-CGA-10 with the following established frailty measures: the Canadian Study of Health and Aging (CSHA) Clinical Frailty Scale (CFS), CSHA rules-based frailty definition and CSHA Function Scale. To evaluate the ability to predict mortality, we tested association between the FI-CGA-10 and validated prognostic indices for mortality: the Lee index and Schonberg index (higher scores reflect a higher risk of mortality). We also examined associations between the FI-CGA-10 and several features commonly seen in frail older adults such as function (Timed Up & Go (TUG) test), cognitive impairment (Mini-Cog), and high comorbidity burden (Charlson Comorbidity Index (CCI)). Results: Of 540 patients (median age 80 years, range 66–96 years), common cancer types were gastrointestinal tract in 37%, hepatobiliary and pancreatic in 22%, and head and neck in 12%. 406 (75%) patients had ECOG PS 0 to 1. The FI-CGA-10 had a right-skewed distribution and was well approximated by the gamma distribution. Overall, 20% of patients were fit, 41% were pre-frail, and 39% were frail. The FI-CGA-10 was highly correlated with CSHA CFS (Pearson's r = 0.83), CSHA rules-based frailty definition (r = 0.67) and CSHA Function Score (r = 0.77). People who were more frail had higher scores on the Lee index (fit: 7.3, prefrail: 8.8, frail: 12.0; p < .0001) and Schonberg index (fit: 10.1, prefrail: 13.1, frail: 15.7; p < .0001), suggesting an increased probability of death. Increasing levels of frailty were significantly associated with a longer TUG (seconds), fit: 11.3, prefrail: 13.0, frail: 26.3; p < .0001, poorer cognitive function (Mini-Cog score, fit: 4.7, prefrail: 4.0, frail: 3.1; p < .0001), and higher comorbidity burden (CCI, fit: 0.8, prefrail: 1.4, frail: 1.9; p < .0001). Conclusions: The FI-CGA-10 is a clinically sensible and construct-validated measure of quantifying frailty from a 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".