Preventing emergency department (ED) visits and hospitalisations of older adults with cognitive impairment compared with the general senior population: what do we know about avoidable incidents? Results from a scoping review
Bibliographic record
Abstract
OBJECTIVES: Older cognitively impaired adults present a higher risk of hospitalisation and mortality following a visit to the emergency department (ED). Better understanding of avoidable incidents is needed to prevent them and the associated ED presentations in community-dwelling adults. This study aimed to synthetise the actual knowledge concerning these incidents leading this population to ED presentation, as well as possible preventive measures to reduce them. DESIGN: A scoping review was performed according to the Arksey and O'Malley framework. METHODS: Scientific and grey literature published between 1996 and 2017 were examined in databases (Medline, Cumulative Index of Nursing and Allied Health, Ageline, Scopus, ProQuest Dissertations/theses, Evidence-based medecine (EBM) Reviews, Healthstar), online library catalogues, governmental websites and published statistics. Sources discussing avoidable incidents leading to ED presentations were included and then extended to those discussing hospitalisation and mortality due to a lack of sources. Data (type, frequency, severity and circumstances of incidents, preventive measures) was extracted using a thematic chart, then analysed with content analysis. RESULTS: 67 sources were included in this scoping review. Five types of avoidable incidents (falls, burns, transport accidents, harm due to self-negligence and due to wandering) emerged, and all but transport accidents were more frequent in cognitively impaired seniors. Differences regarding circumstances were only reported for burns, as scalding was the most prevalent mechanism of injury for this population compared with flames for the general senior population. Multifactorial interventions and implications of other professionals (eg, pharmacist, firefighters) were reported as potential interventions to reduce avoidable incidents. However, few preventive measures were specifically tested in this population. CONCLUSIONS: Primary research that screens for cognitive impairment and involves actors (eg, paramedics) to improve our understanding of avoidable incidents leading to ED visits is greatly needed. This knowledge is essential to develop preventive measures tailored to the needs of older cognitively impaired adults.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".