The Implementation of Frailty Assessment Tools in the Acute Care Setting: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Frailty is a syndrome of increased vulnerability to health stressors that is associated with adverse health outcomes. There is no universally accepted method of measuring frailty, and choosing among the many tools is often confusing for clinicians. Moreover, the acute care setting presents unique challenges to the operationalization of frailty measurement, and implementation into daily clinical practice has been variable. The objective of this scoping review was to map out and synthesize how frailty is being measured and used in the acute care setting. METHODS: We used Arksey and O'Malley's methodological framework for scoping reviews. We searched MEDLINE, EMBASE, CINAHL, SCOPUS, and Google Scholar for primary studies assessing frailty in the acute care setting from inception to May 2023. RESULTS: Our search resulted in 8834 articles, of which 2554 met inclusion criteria. Most articles (75%) were published in the last 5 years. The top three most frequently used methods of frailty measurement were the Frailty Index (41.0%), the Clinical Frailty Scale (23.3%), and the Fried Frailty Phenotype (9.3%). More than one frailty assessment tool was used in 11.2% of studies. While 99.6% of studies measured frailty assessment to evaluate the association of frailty with adverse outcomes or the validity of specific frailty tools, only 0.4% measured frailty to prospectively adapt healthcare provision. CONCLUSION: There is an abundance of evidence demonstrating that frailty in acute care is associated with adverse health outcomes, with relatively scarce evidence on the effect of frailty assessment on prospectively adapting care. Future research focusing on the prospective management of frailty in acute care is needed.
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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 | 0.053 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".