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Record W4388018182 · doi:10.1038/s41746-023-00950-4

A randomized, clinical trial investigating the use of a digital intervention to reduce delirium-associated agitation

2023· article· en· W4388018182 on OpenAlexafffund
Michelle Nicholas, Jessica Wittmann, Monica Norena, Marlena Ornowska, Steven Reynolds

Bibliographic record

Venuenpj Digital Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsCentre for Advancing Health OutcomesFraser HealthSimon Fraser UniversityRoyal Columbian Hospital
FundersMitacs
KeywordsDeliriumIntervention (counseling)MedicineRandomized controlled trialProtocol (science)Clinical trialPopulationStandard of carePhysical therapyEmergency medicinePsychiatryInternal medicineAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

We aimed to determine if a novel digital therapeutic intervention could reduce agitation and unscheduled medication use in an adult delirious acute care population. Delirious participants were randomly allocated (1:1) to receive standard of care plus a single 4-hour exposure to the digital intervention "MindfulGarden", which uses a screen-based delivery to display a nature landscape with dynamic adjustment of screen content in response to movement and sound or standard of care only. Between March 2021 and January 2022, 73 participants were enrolled with 70 completing the trial protocol and included in the final analysis with a mean age of 61 years and 68% being male (35 intervention, 35 control). Mean RASS was significantly lower across the 4-hour study period in the intervention arm 0.3 (0.85) vs 0.9 (0.93), p = 0.01. Exposure to a nature-based dynamic digital intervention showed benefits in agitation reduction.

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)0.002
metaresearch head score (Gemma)0.523
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.523
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.166
GPT teacher head0.408
Teacher spread0.242 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations6
Published2023
Admission routes2
Has abstractyes

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