Operationalizing a Frailty Index from a Standardized Comprehensive Geriatric Assessment
Bibliographic record
Abstract
OBJECTIVES: To construct and validate a frailty index (FI) that is clinically sensible and practical for geriatricians by basing it on a routinely used comprehensive geriatric assessment (CGA) instrument. DESIGN: Secondary analysis of a 3-month randomized, controlled trial of a specialized mobile geriatric assessment team. SETTING: Rural Nova Scotia. Participants were seen in their homes. PARTICIPANTS: Frail older adults, of whom 92 were in the intervention group and 77 in the control group. MEASUREMENTS: A standard CGA form that accounts for impairment, disability, and comorbidity burden was scored and summed as a frailty index (FI-CGA). The FI-GCA was stratified to describe three levels of frailty. Patients were followed for up to 12 months to determine how well the index predicted adverse outcomes (institutionalization or mortality, whichever came first). RESULTS: The three levels of frailty were mild (FI-CGA 0-7), moderate (FI-CGA 7-13), and severe (FI-CGA>13). Demographic and social traits were similar across groups, but greater frailty was associated with worse function (r=0.55) and mental status (r=0.33). Those with moderate and severe frailty had a greater risk of adverse outcomes than those with mild frailty (unadjusted hazard ratio=1.9 and 5.5, respectively). There was no difference between frailty groups in mean 3-month goal-attainment scaling scores. Intrarater reliability was 0.95. CONCLUSION: The FI-CGA is a valid, reliable, and sensible clinical measure of frailty that permits risk stratification of future adverse outcomes.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".