Frailty in Relation to Cognition in Community‐Dwelling Older Adults: Validating the Electronic Frailty Index based on the Comprehensive Geriatric Assessment (eFI‐CGA) in Primary Care
Bibliographic record
Abstract
Abstract Background Frailty can be screened using the Clinical Frailty Scale (CFS) and quantified using a Frailty Index (FI) based on deficit accumulation. Higher degrees of frailty have been associated with greater risks of adverse outcomes, including dementia. Comprehensive Geriatric Assessment (CGA, allows development of individualized care plans for older adults’) has been used to derive an FI‐CGA. To promote effective assessment of frailty outside specialized geriatric services, we developed and are validating an electronic CGA with an automated electronic FI‐CGA (eFI‐CGA). The goal is early detection and management of frailty in home‐living older adults. Here, we examined the characteristics and robustness of the eFI‐CGA assessed by primary‐care physicians and investigated the relationship between the eFI‐CGA and cognition. Method Data were collected as part of the collaborative effort between Fraser Health and Nova Scotia Health to validate the eFI‐CGA. Baseline assessments were conducted at primary care clinics using eFI‐CGA software tools with the eFI‐CGA created following standard procedures. Dementia diagnoses on no cognitive impairment (NCI), mild cognitive impairment (MCI), and dementia were made following DSM‐IV criteria. For robustness testing, multiple interactions of the eFI‐CGA were generated, excluding cognitive measures. Data were analyzed employing regression and descriptive statistics. Result The sample (n = 137; age = 79.2±7.6 years, women = 67.9%; CFS = 3.7±1.3) contained 82.5% NCI and 17.5% MCI/dementia cases. Participants with MCI/dementia were slightly older, less educated, more likely to live alone and take 7+ medications, compared to those with NCI. Sores of the various integrations of the eFI‐CGA were highly correlated. These showed consistent distributions, age relationships, and the maximum values <0.70. The NCI group showed the lowest mean eFI‐CGA scores consistently across the iterations. Close correlations of the eFI‐CGA with the CFS (p<0.001) and the mini‐cog (p<0.010) were observed. Conclusion The characteristics of the eFI‐CGA in primary care were consistent with those widely reported for the FI. A clear association between the eFI‐CGA and cognition was found, even when cognitive measurements were excluded from the eFI‐CGA. Ongoing work will test the generalizability of these findings, with a larger sample size and follow‐up assessments and outcomes, as well as examine how primary care CGA data compared with geriatrician assessments.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".