A Clinimetric Evaluation of Specialized Geriatric Care for Rural Dwelling, Frail Older People
Bibliographic record
Abstract
OBJECTIVE: To test Comprehensive Geriatric Assessment (CGA) as an adjunct to usual care. DESIGN: A randomized controlled trial with 3, 6, and 12 months follow-up. SETTING: Rural communities. PATIENTS: A total of 182 of 265 frail older patients (52 refused, 2 withdrawn, 27 ineligible, 2 deaths) referred by family practitioners with allocation to intervention (n = 95) or usual care (n = 87). INTERVENTION: Three-month implementation of CGA recommendations by a Mobile Geriatric Assessment Team (MGAT) with follow-up assessments at 3, 6, and 12 months. Geriatric nurse assessors, blinded to group assignment, performed each assessment. MAIN OUTCOME MEASURE: Goal Attainment Scaling (GAS). RESULTS: Baseline characteristics were comparable between groups. At 3 months, the intervention group was more likely to attain their goals (GAS total: chi = 46.4 +/- 5.9; GAS outcome chi = 48.0 +/- 6.6) compared with controls (total: chi = 38.7 +/- 4.1; outcome chi = 40.8 +/- 5.6) (P < .001). Standard assessments of function (Barthel index, instrumental activities of daily living), cognition (Mini-Mental State Examination), and quality of life (modified Spitzer quality of life index) showed no difference over 12 months. No difference in survival (intervention: chi = 320 days, SE = 6; control: chi = 294 days, SE = 6; P = .257) or time to institutionalization (intervention: 340 days, SE = 9; control: 342 days, SE = 8; log rank = 0.661; P = .416) were observed. CONCLUSIONS: A MGAT can target rural dwelling, frail older persons, perform in-home CGA, and develop an intervention strategy. Although the intervention did not prolong life or delay institutionalization, clinically important benefits were observed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".