Insight into intraindividual variability across neuropsychological tests and its association with cognitive dysfunction in patients with lupus
Bibliographic record
Abstract
OBJECTIVE: Dispersion, or variability in an individual's performance across multiple tasks at a single assessment visit, has been associated with cognitive dysfunction (CD) in many neurodegenerative and neurodevelopmental disorders. We aimed to compute a dispersion score using neuropsychological battery (NB) tests and determine its association with CD in patients with SLE. METHODS: CD was defined as a z-score of ≤-1.5 on ≥2 domains of the NB. To compute a type of dispersion score known as the intraindividual SD (ISD), the SD of age-adjusted and sex-adjusted z-scores was calculated for each visit in each patient. To estimate the association between ISD and cognitive status (CD and non-CD), we used multilevel logistic regression, adjusting for clinically important covariates. RESULTS: A total of 301 adult patients with SLE completed the NB at baseline, 187 of whom were reassessed at 6 months and 189 at 12 months. CD was observed in 35.2% of patients at baseline, 27.8% at 6 months and 28.0% at 12 months. Prior to covariate adjustment, the mean ISD for non-CD was 1.10±0.31 compared with 1.50±0.70 for CD. After adjusting for ethnicity, education, employment, socioeconomic status and anxiety/depression, there was a statistically significant association between ISD and CD (OR for one-unit increase in ISD: 13.56, 95% CI 4.80 to 38.31; OR for 1/10th-unit increase in ISD: 1.30, 95% CI 1.17 to 1.44). Findings were valid across multiple sensitivity analyses. CONCLUSION: This is the first study to show that patients with SLE who were classified as having CD by the NB had more variability across the NB tests (ie, higher ISD score) compared with those who were not classified as having CD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".