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Record W2752395180 · doi:10.1159/000479679

Screening of Cognitive Impairment in the Dialysis Population: A Scoping Review

2017· review· en· W2752395180 on OpenAlexaboutno aff
Aye Mi San, Balaji Hiremagalur, Wendy Muircroft, Laurie Grealish

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2017
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentDementiaCognitionPopulationPsychologyMedicineAlzheimer's diseasePsychiatryDiseaseInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive impairment in end-stage kidney disease patients on dialysis is increasingly common. This study aimed to review the practice of screening and to evaluate the evidence on cognitive impairment prevalence in this population. METHODS: This scoping review of studies summarises the evidence on cognitive impairment in dialysis populations. The search included the Medline, CINAHL, Embase, PsycINFO, PubMed, and Cochrane Library databases for English-language articles published between 2000 and 2015. A total of 46 articles were reviewed. RESULTS: The studies were of prospective observational design, with the majority conducted in the haemodialysis population. The reported prevalence of cognitive impairment ranged from 6.6 to 51%. Three screening tools were consistently used. CONCLUSION: While cognitive impairment is recognised in the dialysis population, there is paucity of screening data. The design of prospective comparisons ideally includes established screening instruments, particularly the Montreal Cognitive Assessment, to determine the optimal results for this population. Translation of established screening tools to increase the inclusion of people from other cultural and language groups is required. Regular screening can enhance the timing to introduce home-based care support and advance care planning discussions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.373
Teacher spread0.329 · 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

Citations42
Published2017
Admission routes1
Has abstractyes

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