The assessment of frailty in older people in acute care
Bibliographic record
Abstract
Today in Australia we face an ageing population; this will lead to increasing pressure on an already overtaxed health system. It is imperative that we set in place a refined and unified method to assess and predict health outcomes in frail older people As a person ages there is an accumulation of stressors and lifetime risk factors that combine with multi-organ physiological change to give rise to an increased risk of poor outcomes (e.g. death, institutionalisation, falls and iatrogenic disease). This vulnerability can be quantified by a frailty scale. Frailty can be seen as a measurement of the level of support that is required by a person. This measurement is useful as a predictive tool to determine the health services needed by the patient and for research purposes to assist in public health planning. In recent years effort has been made to determine the predictive strength of these scales and their efficacy in improving patients’ final outcomes. There are a number of different frailty scales currently in use, each with different emphases and measuring different outcomes. The majority of studies looking at frailty to date have been conducted in the community [1,2]. Of those that have looked at patients in a hospital setting [3,4], there has been a trend to use scales that are multifaceted, taking into account the complex nature of frailty, such as the Reported Edmonton Scale reported by Hilmer et al. in this edition of the Journal. The Reported Edmonton Scale has several attractive features: it can be assessed by interviewers without medical training, making it easier to use for larger studies; it has been designed to capture the pre-morbid frailty despite being collected in a hospital setting; and it correlated well with a global assessment of frailty as assessed by a geriatrician involved in comprehensive geriatric assessment. Given that the Reported Edmonton Scale incorporates components of cognition, independence, polypharmacy and comorbidity, it is no surprise that the scale correlated well with established methods of measurement for these domains. However, this finding does emphasise that the scale reflects the complex multi-faceted nature of frailty. Where to next? The challenge for the Geriatric Medicine research community is to identify which frailty measure will be the most useful in our clinical trial, epidemiology and observational research practice. At this stage it is unclear which measure will survive. However, incorporation of frailty measures into routine research must be a good thing, as it implicitly acknowledges that medical research should and must include the frail older person.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".