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Record W1980851493 · doi:10.1177/0961203311405374

Cognitive dysfunction in SLE: development of a screening tool

2011· article· en· W1980851493 on OpenAlexaboutno aff
Tara Ballav Adhikari, A Piatti, Michael E. Luggen

Bibliographic record

VenueLupus · 2011
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineNeuropsychological assessmentNeuropsychologyGold standard (test)CognitionNeuropsychological testingCognitive impairmentInternal medicineNeuropsychological testPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive dysfunction (CD) is among the most common neuropsychiatric manifestations of systemic lupus erythematosus (SLE). There are two methods which have been used to detect CD in patients with SLE: traditional neuropsychological tests (NPT) and the Automated Neuropsychological Assessment Metrics (ANAM). Both are time-consuming and neither is readily available for screening purposes. PURPOSE: The aim of our study was to evaluate the Montreal Cognitive Assessment (MoCA) test as a screening tool for detection of CD in SLE. Methods. SLE patients fulfilling ACR criteria were administered the ANAM, a computerized test battery which measures various cognitive domains and the MoCA, a one-page, performance-based screening test designed to detect mild cognitive impairment in the elderly. With the ANAM as the gold standard, the performance characteristics of the MoCA were assessed. RESULTS: In total, 44 patients were evaluated. Of these, 11 (25%) were identified by the ANAM as being impaired in comparison with 13 (29.5%) by the MoCA. The scores were significantly correlated (r = 0.57, p < 0.001). Using the standard cutoff of 26, the sensitivity of MoCA was 83% and specificity 73%. CONCLUSION: The MoCA appears to be a promising screening tool for the detection of CD in SLE both for epidemiologic studies and for routine clinical care.

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 score0.419
Threshold uncertainty score0.294

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 head0.077
GPT teacher head0.305
Teacher spread0.228 · 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

Citations59
Published2011
Admission routes1
Has abstractyes

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