MétaCan
Menu
← Back to cohort
Record W4408111192 · doi:10.1101/2025.02.28.25323113

A Systematic Review of Interventions for Persons Living With Dementia: The Geriatric ED Guidelines 2.0

2025· review· en· W4408111192 on OpenAlexaff
Sangil Lee, Michelle Suh, Luna Rugsdale, Justine Seidenfeld, James David van Oppen, Lauren Lapointe‐Shaw, James A. Jaramillo, Annie Wescott, Maura Kennedy, Lauren Cameron Comasco, Christopher R. Carpenter, Teresita M. Hogan, Shan W. Liu

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionMedicineObservational studyEmergency departmentDeliriumDementiaSystematic reviewMEDLINERandomized controlled trialCritical appraisalCochrane LibraryPopulationFamily medicineGerontologyAlternative medicineIntensive care medicinePsychiatryDisease

Abstract

fetched live from OpenAlex

Abstract Background The increasing prevalence of dementia poses significant challenges for emergency department (ED) care, as persons living with dementia (PLWD) more frequently experience adverse outcomes such as delirium, prolonged stays, and higher mortality rates. Despite advancements in care strategies, a critical gap remains in understanding how ED interventions impact outcomes in this vulnerable population. This systematic review aims to identify evidence-based ED care interventions tailored to PLWD to improve outcomes. Methods A systematic review was conducted in Ovid MEDLINE, Cochrane Library (Wiley), Scopus (Elsevier), and ProQuest Dissertations & Theses Global through September 2024. The review protocol was registered on PROSPERO (CRD42024586555). Eligible studies included randomized controlled trials, observational studies, and quality improvement initiatives focused on ED interventions for PLWD. Data extraction and quality assessment were performed independently by two reviewers, with disagreements resolved through discussion. Outcomes included patient satisfaction, ED revisits, functional decline, and mortality. Results From 3,305 screened studies, six met the inclusion criteria. Interventions included nonpharmacologic therapies (e.g., music and light therapy), specialized geriatric ED units, and assessment tools, such as for pain. Tailored interventions including geriatric emergency units and community paramedic care transitions were effective in reducing 30-day ED revisits and hospitalizations. However, heterogeneity in study designs and outcomes precluded meta-analysis. Risk of bias ranged from low to moderate. Conclusion This review underscores the urgent need for standardized and evidence-based interventions in ED settings for PLWD. Approaches including multidisciplinary care models and non-pharmacologic therapies demonstrated potential for improving outcomes. Future research should prioritize consistent outcome measures, interdisciplinary collaboration, and person-centered care strategies to enhance the quality and equity of ED services for PLWD. Key Points Tailored interventions such as geriatric ED units and community paramedic care transitions significantly reduce ED revisits and hospital admissions among persons living with dementia. Non-pharmacologic therapies, including music and light interventions, show potential for improving patient outcomes, though results are heterogeneous and require further validation. The review highlights the urgent need for standardized protocols and interdisciplinary approaches to enhance emergency care for this vulnerable population. Why does this paper matter? This paper addresses critical knowledge gaps concerning emergency care for persons living with dementia, offering evidence-based insights to improve outcomes and guide the development of standardized, person-centered interventions in ED settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.078
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0150.012
Bibliometrics0.0150.011
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0040.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.061
GPT teacher head0.388
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicIntensive Care Unit Cognitive Disorders→French-language works237,207→