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Record W3044337292 · doi:10.1186/s13104-020-05195-y

The effect of blue dishware versus white dishware on food intake and eating challenges among residents living with dementia: a crossover trial

2020· article· en· W3044337292 on OpenAlexafffund
Rachael Donnelly, Cindy Wei, Jill Morrison, Heather Keller

Bibliographic record

VenueBMC Research Notes · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
FundersMitacs
KeywordsCrossover studyMedicineMealMalnutritionPsychological interventionEnvironmental healthFood intakeFood choiceDementiaGerontologyInternal medicineAlternative medicinePlaceboPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Residents living with dementia (RLWD) often experience changes in their visual perception, which could reduce food intake. Inadequate food intake is known to cause malnutrition, which increases the risk of hospitalization, morbidity, and mortality. This study evaluated the effectiveness of using blue dishware compared to white dishware to improve food intake and mitigate eating challenges among 18 RLWD (mean age 84.6 ± 7.9 years, 72.2% female). RESULTS: A within-within person crossover design determined differences in food intake and eating challenges between blue and white dishware conditions. Five participants responded to the blue dishware and increased their average food intake by ≥ 10%. Responders were not different from non-responders in terms of demographic or health characteristics. The proportion of eating challenges experienced was not significantly different between the blue and white dishware conditions. Percent food intake was significantly greater at lunch (83.5 ± 19.0%) compared to dinner (75.8 ± 22.1%; p < 0.0001), regardless of dishware condition. However, there were no significant differences for food intake between the dishware conditions, even after matching food choices. Promoting food intake and reducing eating challenges in RLWD likely needs multi-component interventions targeting meal quality, meal access, and mealtime experience. Trial registration ClincialTrials.gov Identifier: NCT04298788. Retrospectively registered: 6 March 2020, https://clinicaltrials.gov/ct2/show/NCT04298788?term=NCT04298788&draw=2&rank=1 .

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.206
GPT teacher head0.425
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2020
Admission routes2
Has abstractyes

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