Neurocognitive impairment in childhood‐onset systemic lupus erythematosus: Measurement issues in diagnosis
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of neurocognitive impairment (NCI) in childhood-onset systemic lupus erythematosus (cSLE) by comparing published classification criteria, and to examine associations between NCI, disease characteristics, psychosocial well-being, and intelligence. METHODS: cSLE patients and ethnicity- and age-matched healthy controls completed a neuropsychological research battery, and results were categorized by 3 different NCI classification criteria with different cutoff scores (e.g., >2, 1.5, or 1 SD below the mean) and the number of required abnormal tests or domains. RESULTS: Forty-one cSLE subjects and 22 controls were included. Subjects were predominantly female (~70%) and Hispanic (∼70%). Executive functioning, psychomotor speed, and fine motor speed were most commonly affected. Method 1 classified 34.1% of cSLE subjects with NCI compared to method 2 (14.6% with decline and 7.3% with NCI) and method 3 (63.4% with NCI). The prevalence of NCI was not significantly different between the controls and patients using any of the categorization methods. NCI was not associated with SLE disease activity or characteristics or with depression. Using method 3, patients in the cognitive impairment group reported significantly lower quality of life estimates (69.7 versus 79.3; P = 0.03). Below average intellectual functioning (intelligence quotient <90) differentiated the number of test scores >1 and >1.5 SDs, but not >2 SDs below the mean. CONCLUSION: NCI was prevalent in cSLE, but varied according to the chosen categorization method. A similar proportion of cSLE patients and controls had NCI, reinforcing the importance of studying an appropriate control group. Categorical classification (i.e., impaired/nonimpaired) may oversimplify the commonly observed deficits in cSLE.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".