Cognitive Performance Scores for the Pediatric Automated Neuropsychological Assessment Metrics in Childhood‐Onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To develop and initially validate a global cognitive performance score (CPS) for the Pediatric Automated Neuropsychological Assessment Metrics (PedANAM) to serve as a screening tool of cognition in childhood lupus. METHODS: Patients (n = 166) completed the 9 subtests of the PedANAM battery, each of which provides 3 principal performance parameters (accuracy, mean reaction time for correct responses, and throughput). Cognitive ability was measured by formal neurocognitive testing or estimated by the Pediatric Perceived Cognitive Function Questionnaire-43 to determine the presence or absence of neurocognitive dysfunction (NCD). A subset of the data was used to develop 4 candidate PedANAM-CPS indices with supervised or unsupervised statistical approaches: PedANAM-CPSUWA , i.e., unweighted averages of the accuracy scores of all PedANAM subtests; PedANAM-CPSPCA , i.e., accuracy scores of all PedANAM subtests weighted through principal components analysis; PedANAM-CPSlogit , i.e., algorithm derived from logistic models to estimate NCD status based on the accuracy scores of all of the PedANAM subtests; and PedANAM-CPSmultiscore , i.e., algorithm derived from logistic models to estimate NCD status based on select PedANAM performance parameters. PedANAM-CPS candidates were validated using the remaining data. RESULTS: PedANAM-CPS indices were moderately correlated with each other (|r| > 0.65). All of the PedANAM-CPS indices discriminated children by NCD status across data sets (P < 0.036). The PedANAM-CPSmultiscore had the highest area under the receiver operating characteristic curve (AUC) across all data sets for identifying NCD status (AUC >0.74), followed by the PedANAM-CPSlogit , the PedANAM-CPSPCA , and the PedANAM-CPSUWA , respectively. CONCLUSION: Based on preliminary validation and considering ease of use, the PedANAM-CPSmultiscore and the PedANAM-CPSPCA appear to be best suited as global measures of PedANAM performance.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".