19EVALUATION OF A STAND ALONE FRAILTY UNIT
Bibliographic record
Abstract
Topic The British Geriatric Society's “Silver Book” and Fit for Frailty recommends quality standards of care. Frailty Units are now functioning within Emergency Departments (ED), Medial Assessment Units (MAU) or alongside geriatric wards. Our District General Hospital does not have an acute or general geriatric service. Intervention A successful pilot In-Reach Single Comprehensive Geriatric Encounter (IRSCGE) service onto MAU lead to the creation of a 15 bedded acute frailty unit (AFU) in September 2014, independent of ED or MAU. The Bournemouth criteria were used to identify suitable patients. Patients received Consultant led comprehensive geriatric assessment, daily interventions and discharge planning along designated pathways. Improvement Data were available from 72 patients (median age 86.00, IQR 80.75 to 91.00; 61% female) who had a median Edmonton Frailty Score (EFS) of 9(IQR 6-10.3). Median numbers of co-morbidities were 4: 26% dementia; 39% falls; 69% polypharmacy; and 24% delirium. A median 3 geriatric domains were identified per patient. Advanced Care Planning occurred in 17% and 11% died during admission. 28-day readmission rate was 10.9% (8.4% Trust average). Comparisons between AFU and IRSCGE showed a trend in AFU group toward lower total LOS (median 7 vs 9, p = 0.096) and 3 month mortality (29% vs 44%, p = 0.068), possibly due to AFU group being statistically less old, less frail and with less delirium. However, a significantly higher proportion were discharged to their usual residence in the AFU group (63% vs 38%, p = 0.021) suggesting the AFU was targeting patients who would benefit most.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".