198 How comprehensive is our Comprehensive Geriatric Assessment in clinical practice?
Bibliographic record
Abstract
Abstract Background No gold standard exists for what should be included within a Comprehensive Geriatric Assessment (CGA). Consensus is that a CGA assessment should assess physical, functional, psychological and social well-being. We sought to examine the specific content of CGA that are being delivered across integrated care teams in Ireland. Methods We completed a cross sectional study of what domains are included in different integrated care CGA proformas. All operational leads for each integrated care hub were contacted and invited to share their local CGA. We examined what components across the domains of physical, psychological, functional and social assessment were included. Results We examined 16 different CGAs. The median length of a CGA was 14 pages (range: 4–28 pages). Common areas in all CGAs included assessments of frailty, cognition, mobility, falls, continence and social assessment, but there was variability in how these assessments were carried out. The Rockwood Clinical frailty scale the most common diagnostic tool for frailty (15/16 CGAs) with sarcopenia assessed in 75% of CGAs. The 4AT tool was used in 56% of CGAs, with a more detailed tool (e.g MMSE or MOCA) used in 56% of CGAs. Mood was assessed in 15 CGAs, sleep assessed in 10 CGAs and pain assessed in 7 CGAs. The widest variability was in the social assessment section with inconsistent assessments of caregiver strain (completed in 50%) and assessments of formal advanced care supports such as enduring power of attorney included in 44% of CGAs. Sexual health was not explicitly addressed in any CGA. Conclusion While there is considerable overlap with the core components of a CGA across integrated care sites there is significant variability across individual sites. Our results highlight the opportunity for consensus building across different integrated care teams to harmonise the delivery of CGA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".