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Record W2087211547 · doi:10.1002/acr.20152

A proposed framework to standardize the neurocognitive assessment of patients with pediatric systemic lupus erythematosus

2010· article· en· W2087211547 on OpenAlexaff
Gail Ross, Frank Zelko, Marisa S. Klein‐Gitelman, Deborah M. Levy, Eyal Muscal, Laura E. Schanberg, Kelly K. Anthony, Hermine I. Brunner

Bibliographic record

VenueArthritis Care & Research · 2010
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsNeurocognitiveMedicineCognitionCohortCognitive testNeuropsychological assessmentNeuropsychologyCognitive skillStandardized testPediatricsPsychiatryPsychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop and propose a standardized battery of neuropsychological tests for the assessment of cognitive functioning of children and adolescents with pediatric systemic lupus erythematosus (SLE). METHODS: A committee of health care professionals involved in the assessment of pediatric SLE patients reviewed the literature to identify cognitive domains most commonly affected in pediatric SLE and in adult SLE. They then reviewed the standardized tests available for children and adolescents that assess the cognitive domains identified. Through a structured consensus formation process, the committee considered the psychometric characteristics and durations of the tests. RESULTS: A test battery was developed that appears suitable to provide a comprehensive assessment of cognitive domains commonly affected by pediatric SLE within a 2.5-hour period. CONCLUSION: It is hoped that the consistent use of this reliable and efficient battery increases the practicality of routine evaluations in pediatric SLE, enabling between-cohort comparisons and facilitating the longitudinal assessment of individual patients over time.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.301
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.017
GPT teacher head0.345
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.

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

Citations42
Published2010
Admission routes1
Has abstractyes

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