Which acute deterioration tools are used in long-term care facilities and how have they been evaluated? A scoping review
Bibliographic record
Abstract
Abstract Background Acute deterioration describes a rapid decline in health due to short-duration illnesses. This is an important topic for older adults living in long-term care facilities (LTCF). Signs of acute deterioration are often subtle, and there is no standardised system to manage it. The aim of this review is to scope the range of deterioration tools used in LTCFs, and to describe how they have been evaluated. Methods A scoping review was conducted in accordance with the Joanna Briggs Institute methodology. Searches of five (MEDLINE, APA PsycInfo, Embase, CINAHL, HMIC) electronic databases (2013–2023, updated 2025) and relevant websites were followed by title/abstract (by two authors independently) and full-text screening. Eligible studies involved tools used to manage acute deterioration for adults > 65 years in LTCFs. Experimental and observational study designs were eligible, including quality improvement projects. No country or language restrictions were imposed. A narrative synthesis was conducted. Results Twenty-six studies were included (23 peer-reviewed articles, two conference abstracts, one dissertation) after screening 5958 articles. A majority were from the UK (n = 10) and USA (n = 9), with small numbers from other high-income countries ((Australia (n = 2), Canada (n = 2), Sweden (n = 2), Switzerland (n = 1)). Studies employed a wide range of methodologies, with only one randomised study, and tools were frequently evaluated as part of multi-faceted interventions. The majority of studies described an intervention in which SBAR (situation-background-action-recommendation) (n = 15), National Early Warning Scores (n = 7) or STOP AND WATCH (n = 4) were a component. Studies used quantitative (n = 21) and qualitative (n = 9) methods to evaluate tools. Outcome measures were heterogeneous, with no data on resident experience. The majority of studies concluded potential benefit from using deterioration tools. There is some evidence that LTCF staff perceive tools, especially SBAR, as improving confidence in managing acute deterioration and aiding communication with external healthcare professionals. Conclusion Despite policy drivers to use deterioration tools in LTCFs, there is no robust evidence to support this. Direct benefits for resident care have not been demonstrated. Further research is required to compare tools to standard care, measure the impact on resident experience, and to determine if deterioration tools should become part of routine care in LTCFs.
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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 | 0.046 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".