The BREV neuropsychological test: Part II. Results of validation in children with epilepsy
Bibliographic record
Abstract
The Battery for Rapid Evaluation of Cognitive Functions (Batterie Rapide d'Evaluation des Fonctions Cognitives: BREV) is a quick test to screen children with higher‐functioning disorders and to define the patterns of their disorders. After standardization tests in 500 normally developing children aged 4 to 8 years, validation consisted of comparative evaluation of the specificity and sensitivity of the BREV with a wide reference battery in 202 children with epilepsy (108 males, 94 females; mean age 6 years 6 months, SD 1 year 8 months). Children were divided into 10 age groups from 4 to 8 years of age and represented eight epileptic syndromes. The reference battery included verbal and non‐verbal intelligence assessment using the Wechsler scale, oral language assessment with a French battery for oral language study, drawing with the Rey figure, verbal and visuo‐spatial memory with the McCarthy scale subtest and the Rey figure recall, and educational achievement with the Kaufman subtests. Every function evaluated with the BREV was significantly correlated with the reference battery testing a similar function (p=0.01 to 0.001). Specificity and sensitivity of the BREV verbal and non‐verbal scores were correlated with those of the Wechsler scale in more than 75% of children. The BREV, therefore, appears to be a reliable test which has been carefully standardized and validated and is valuable in screening for cognitive impairment in children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".