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Record W3164343072 · doi:10.1177/07334648211018032

Effectiveness of Interprofessional Consultation-Based Interventions for Delirium: A Scoping Review

2021· review· en· W3164343072 on OpenAlexafffund
Caitlin Monaghan, Grace Martin, Jason Kerr, Mary-Lynn Peters, Judith Versloot

Bibliographic record

VenueJournal of Applied Gerontology · 2021
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMcMaster UniversityTrillium Health CentreUniversity of Toronto
FundersMedical Psychiatry Alliance
KeywordsCINAHLPsychological interventionDeliriumPsycINFOOperationalizationMedicineMEDLINEIntervention (counseling)NursingPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Interprofessional geriatric consultation teams and multicomponent interventions are established models for delirium care. They are combined in interprofessional consultative delirium team interventions; however, insight into this novel approach is lacking. OBJECTIVE: To describe the effectiveness and core components of consultation-based interventions for delirium. METHOD: Ovid MEDLINE, EMBASE, PsycINFO, CINAHL, and ProQuest. Data on core intervention components, outcomes, facilitators, and barriers were extracted. RESULTS: 10 studies were included. Core intervention components were systematic delirium screening, ongoing consultation, implementation of non-pharmacologic and pharmacological interventions, and staff education. Of the included studies, 1/6 found a significant reduction in delirium incidence, 1/2 a reduction in delirium duration, and 2/3 found a reduction in falls. Facilitators and barriers to implementation were discussed. CONCLUSION: There was consistency in team structure and core components, however intervention operationalization and effectiveness varied widely. There is some evidence that this model is effective for reducing delirium and its sequelae.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.466
Teacher spread0.367 · 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 teacher head, not a consensus.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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