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Thickened fluids for people with dementia in residential aged care facilities: a comprehensive systematic review

2009· article· en· W2160363934 on OpenAlexaff
Sonia Hines, Judy McCrow, Jenny Abbey, Samuel Gledhill

Bibliographic record

VenueThe JBI Database of Systematic Reviews and Implementation Reports · 2009
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsVictoria Park
Fundersnot available
KeywordsDementiaDysphagiaMedicineOlder peopleMedical prescriptionGerontologyNursingSurgeryPathologyDisease

Abstract

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Background Dementia is an umbrella term for a large group of conditions that cause a progressive decline in a person's functioning. Dementia is a progressive condition which causes cognitive and functional decline and, as part of this, the ability to swallow diminishes, a condition known as dysphagia[1]. Aspiration of fluids is a common result of dysphagia and is a significant problem, both in terms of its effect on the patient and its cost to the community. In order to increase the viscosity of drinking fluids and thereby minimise the likelihood of aspiration, people with dementia living in residential aged care facilities (RACFs) are often prescribed thickened fluids. Objectives The overall objective of this comprehensive systematic review was to establish best practice in relation to thickened fluids for people living in RACFs to: establish factors associated with the prescription and administration of thickened fluid for people with dementia living in RACFs; assess the effectiveness of administering thickened fluids for people with dementia in RACFs in terms of adequate hydration, mortality, morbidity and patient comfort; identify attitudes of people with dementia in RACFs and their family/carers regarding the administration of thickened fluids; and identify attitudes of staff regarding the administration of thickened fluids for people with dementia in RACFs. Search Strategy English language articles published from 1995 to 2008 were sought in a comprehensive search of an extensive range of databases, online sources and unpublished literature. Selection criteria The review considered all types of studies which included the oral administration of fluids with the addition of commercial thickening agent to the liquid which may include: energy/nutritional supplements, juice, water, tea, coffee, cordial, milk drinks, etc., for the purpose of increasing the viscosity of the liquid to minimise the chance of aspiration for persons with dementia in residential aged care. Data collection and analysis Two independent reviewers assessed the eligibility of each study for inclusion into the review, critically appraised the study quality and extracted data using standardised tools. Any disagreements were adjudicated by a third reviewer. Main findings Nine papers[2-10] recommend the use of thickened fluids as a strategy to maintain adequate fluid intake for demented persons with dysphagia in RACFs. (Level III.c - IV evidence) Four papers[1, 4, 8, 9] directly recommend thickened fluids as an effective method of maintaining hydration for persons with dementia. (Level IV evidence) One study[11] found that the use of thickened fluids was generally acceptable to older persons in the event of dementia and dysphagia and was more acceptable to the participants than the prospect of enteral feeding. (Level IV evidence) One study found that the practice of adding infant cereals to fluids as a thickening agent cannot be recommended due to concerns over dehydration and folate deficiency[12]. (Level IV evidence) Conclusion There appears to be little specific data on the effectiveness of thickened fluids for people with dementia in residential aged care. Most included studies had mixed populations of demented and non-demented residents, making dementia-specific results impossible to quantify. From the retrieved data, evidence-based best practices cannot be concluded. It may, however, be cautiously inferred that thickened fluids may be effective for residents with dementia if set guidelines are instituted.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.065
GPT teacher head0.433
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.

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

Citations5
Published2009
Admission routes1
Has abstractyes

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