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PREVALENCE OF COGNITIVE IMPAIRMENT IN PATIENTS ATTENDING PRE‐DIALYSIS CLINIC

2008· article· en· W2053069652 on OpenAlexaboutno aff
Rebekah S. Nulsen, Muhammad M. Yaqoob, Althea Mahon, Meagan Stoby‐Fields, Mike Kelly, Mira Varagunam

Bibliographic record

VenueJournal of Renal Care · 2008
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersDialysis Clinics
KeywordsMedicineDialysisPeritoneal dialysisNephrologyCognitionCognitive impairmentHemodialysisInternal medicineRenal replacement therapyMontreal Cognitive AssessmentKidney diseaseIntensive care medicinePhysical therapyDiseasePsychiatry

Abstract

fetched live from OpenAlex

Approximately 20-30% of patients on renal replacement therapy (RRT) have cognitive impairment. Less is known about the prevalence of cognitive impairment in patients with advanced kidney disease awaiting the initiation of dialysis. Routine cognitive assessment was implemented in the pre-dialysis clinic, which enabled the Nephrologist and Pre-dialysis Nurse to identify those patients with impaired cognitive function and utilise this information to assess the suitability for self-care treatments, such as peritoneal dialysis, as well as to adapt information to meet their needs. Subsequently, a cross-sectional single-centre audit was undertaken to identify the prevalence of cognitive impairment in 132 consecutive new referrals to the pre-dialysis clinic using the Mini-mental State Examination (MMSE). Twenty percent (95% CI = 0.13, 0.27) were classified as cognitively impaired. Those with cognitive impairment were significantly older, and had lower eGFR and higher serum creatinine. It can be concluded that approximately 1 in 5 patients attending the pre-dialysis clinic has cognitive impairment, which may not be apparent on a routine clinical history. Cognitive function assessment is recommended for all, but particularly to the older patient, before advising on choice of dialysis modality or opting for conservative treatment.

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 score0.004
Threshold uncertainty score0.317

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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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