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Record W2105768398

End-of-life issues in advanced dementia: Part 2: management of poor nutritional intake, dehydration, and pneumonia.

2015· article· en· W2105768398 on OpenAlexaff
Marcel Arcand

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsDementiaMedicineIntensive care medicineGuidelinePneumoniaMEDLINEArtificial nutritionParenteral nutritionDelphi methodStage (stratigraphy)DiseaseInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To answer frequently asked questions about management of end-stage pneumonia, poor nutritional intake, and dehydration in advanced dementia. SOURCES OF INFORMATION: Ovid MEDLINE was searched for relevant articles published until February 2015. No level I studies were identified; most articles provided level III evidence. The symptom management suggestions are partially based on recent participation in a Delphi procedure to develop a guideline for optimal symptom relief for patients with pneumonia and dementia. MAIN MESSAGE: Feeding tubes are not recommended for patients with end-stage dementia. Comfort feeding by hand is preferable. Use of parenteral hydration might be helpful but can also contribute to discomfort at the end of life. Withholding or withdrawing artificial nutrition and hydration is generally not associated with manifestations of discomfort if mouth care is adequate. Because pneumonia usually causes considerable discomfort, clinicians should pay attention to symptom control. Sedation for agitation is often useful in patients with dementia in the terminal phase. CONCLUSION: Symptomatic care is an appropriate option for end-stage manifestations of advanced dementia. The proposed symptom management guidelines are based on a literature review and expert consensus.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.045
GPT teacher head0.294
Teacher spread0.249 · 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

Citations50
Published2015
Admission routes1
Has abstractyes

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