Reversing Frailty Levels in Primary Care Using the CARES Model
Bibliographic record
Abstract
BACKGROUND: The purpose of this manuscript was to evaluate the effectiveness of the Community Actions and Resources Empowering Seniors (CARES) model in measuring and mitigating frailty among community-dwelling older adults. METHODS: The CARES model is based on a goal-oriented multidisciplinary primary care plan which combines a comprehensive geriatric assessment (CGA) with health coaching. A total of 51 older adults (82 ± 7 years; 33 females) participated in the pilot phase of this initiative. Frailty was measured using the Clinical Frailty Scale (CFS) and the Frailty Index (FI-CGA) at baseline and at six-month follow-up. RESULTS: The FI-CGA at follow-up (0.21 ± 0.08) was significantly lower than the FI-CGA at baseline (0.24 ± 0.08), suggesting an average reduction of 1.8 deficits. Sixty-one per cent of participants improved their FI-CGA and 38% improved CFS categories. Participants classified as vulnerable/frail at baseline were more responsive to the intervention compared to non-frail participants. CONCLUSION: Pilot data showed that it is feasible to assess frailty in primary care and that the CARES intervention might have a positive effect on frailty, a promising finding that requires further investigations. General practitioners who participate in the CARES model can now access their patients' FI-CGA scores at point of service through their electronic medical records.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".