238 HIGH BURDEN OF FRAILTY AMONG PATIENTS AGED OVER 65 YEARS IN A TERTIARY REFERRAL HOSPITAL
Bibliographic record
Abstract
Background Frailty as a concept is associated with key clinical syndromes including falls, polypharmacy and worsening mobility. Screening and measurement of frailty is recommended as part of comprehensive geriatric assessment of older patients. However, there is no clear consensus on its definition and most appropriate screening tool. Prevalence rates for frail older patients presenting in the acute hospital setting range from 4–11% depending on the definition and screening tool used. Methods Assessment of frailty was retrospectively performed on all medical patients over 65 years admitted to University Hospital Limerick in a 1 week period. The 9 point Edmonton Frailty Score in app version (Doctot) was utilised as it has been validated for acute medical unit patients. The predicted length of stay of each patient was correlated to frailty scores using this medical software application tool. Results Over 7 consecutive days, there were 226 medical admissions in UHL; 118 were over 65 years. Of the 51 patients screened, 21% patients were assessed as mild frailty, 13% moderate frailty, and 27% were deemed severely frail.50.9% were male. Median age was 75.6 years. Predicted length of stay for patients scoring mild frailty percentages was 7.3 days with percentage mortality at follow up of 2.4%, moderate scores had hospital LOS 9.9 days with mortality 2.9% and severe scores had hospital LOS 15.6 days and 12.9% mortality at follow up. Conclusions A key tenet of the ED Taskforce Report 2015 is the immediate creation of rapid access elderly assessment and treatment services for elderly patients in all acute hospitals. In this regard, rapid mobile assessment tools of frailty can augment national care policies to identify vulnerable patients. There is a high burden of frailty in acute medical wards and utilising predictive length of stay can aid more efficient discharge planning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".