EARLY DETECTION AND MANAGEMENT OF FRAILTY IN PRIMARY CARE: VALIDATION OF THE EFI-CGA WITH ELECTRONIC HEALTH RECORDS
Bibliographic record
Abstract
Abstract Background Frailty is common in older adults and associated with many adverse outcomes. To promote early detection and management of frailty outside specialized geriatric services, we developed an electronic Frailty Index based on a Comprehensive Geriatric Assessment (eFI-CGA) in electronic health records. Here, we compare the eFI-CGA assessments between family physicians (FP) and geriatricians (GM). Methods Data from community-dwelling older adults were collected as part of the collaborative effort between Fraser Health and Nova Scotia Health to validate the eFI-CGA. The eFI-CGA was created following a standard procedure based on understanding deficit accumulation. A FP and a GM assessed each patient independently. Characteristics of the eFI-CGA were examined for each physician group using descriptive statistics and correlation analysis. FP-GM inter-rater reliability was tested using Cohen’s Kappa. Results The first 30 cases were aged 80.8±5.2 years; 7% were women; with 12.9±2.8 years of education; 17% lived alone. Mild cognitive impairment or dementia was present in 20% participants. The mean clinical frailty scale (CFS) was 3 and the mean eFI-CGA was 0.20 by both FP and GM ratings. The CFS and eFI-CGA were closely correlated (r=0.76 for FP and r=0.71 for GM, p<.001). The eFI-CGA also showed an age correlation (r values >0.37, p values <.050). The average intraclass correlation coefficient was 0.79 for CFS and 0.90 for eFI-CGA (each p<.001). Conclusion Frailty data collected in primary care are highly comparable with geriatrician assessments. Ongoing work will test the generalizability of these findings using a larger sample with follow-up and outcomes evaluations.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".