MétaCan
Menu
Back to cohort
Record W3009467844 · doi:10.1681/asn.2019100988

Evaluation of Screening Tests for Cognitive Impairment in Patients Receiving Maintenance Hemodialysis

2020· article· en· W3009467844 on OpenAlexaboutno aff
David A. Drew, Hocine Tighiouart, Jasmine Rollins, Sarah Duncan, Seda Babroudi, Tammy Scott, Daniel E. Weiner, Mark J. Sarnak

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMontreal Cognitive AssessmentNeurocognitiveCognitionMedicineCohortPopulationMini–Mental State ExaminationCognitive testTrail Making TestHemodialysisPhysical therapyPsychiatryCognitive impairmentInternal medicine

Abstract

fetched live from OpenAlex

Significance Statement Cognitive impairment is common among individuals receiving maintenance hemodialysis, but few data exist regarding how well screening tests for cognitive function perform in this population. The authors assessed the ability of the Mini Mental State Examination, the Modified Mini Mental State Examination, the Montreal Cognitive Assessment, the Trail Making Test Part B, the Mini-Cog test, and the Digit Symbol Substitution Test to predict severe cognitive impairment in a cohort of 150 patients on dialysis whose cognitive status had been first defined with a battery of neurocognitive tests. The Montreal Cognitive Assessment was the best-performing overall screening test, and the authors recommend it as the preferred test to screen for severe cognitive impairment in patients receiving maintenance hemodialysis. Identification of such impairment may then facilitate optimal medical management and discussion of relevant issues with patients and family members. Background Neurocognitive testing shows that cognitive impairment is common among patients receiving maintenance hemodialysis. Identification of a well performing screening test for cognitive impairment might allow for broader assessment in dialysis facilities and thus optimal delivery of education and medical management. Methods From 2015 to 2018, in a cohort of 150 patients on hemodialysis, we performed a set of comprehensive neurocognitive tests that included the cognitive domains of memory, attention, and executive function to classify whether participants had normal cognitive function versus mild, moderate, or severe cognitive impairment. Using area-under-the-curve (AUC) analysis, we then examined the predictive ability of the Mini Mental State Examination, the Modified Mini Mental State Examination, the Montreal Cognitive Assessment, the Trail Making Test Part B, the Mini-Cog test, and the Digit Symbol Substitution Test, determining each test’s performance for identifying severe cognitive impairment. Results Mean age was 64 years; 61% were men, 39% were black, and 94% had at least a high-school education. Of the 150 participants, 21% had normal cognitive function, 17% had mild cognitive impairment, 33% had moderate impairment, and 29% had severe impairment. The Montreal Cognitive Assessment had the highest overall predictive ability for severe cognitive impairment (AUC, 0.81); a score of ≤21 had a sensitivity of 86% and specificity of 55% for severe impairment, with a negative predictive value of 91%. The Trails B and Digit Symbol tests also performed reasonably well (AUCs, 0.73 and 0.78, respectively). The other tests had lower predictive performances. Conclusions The Montreal Cognitive Assessment, a widely available and brief cognitive screening tool, showed high sensitivity and moderate specificity in detecting severe cognitive impairment in patients on maintenance hemodialysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.314
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations80
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicDialysis and Renal Disease ManagementFrench-language works237,207