Frequency of Abnormal Scores on the Neuropsychological Assessment Battery Screening Module (S-NAB) in a Mixed Neurological Sample
Bibliographic record
Abstract
The Neuropsychological Assessment Battery (NAB; Stern & White, 2003 Stern , R. A. , & White , T. ( 2003 ). Neuropsychological Assessment Battery . Lutz , FL : Psychological Assessment Battery . [Google Scholar]; White & Stern, 2003 White , T. , & Stern , R. A. ( 2003 ). Neuropsychological Assessment Battery: Psychometric and technical manual . Lutz , FL : Psychological Assessment Resources . [Google Scholar]) is a comprehensive, modular battery of tests comprised of the following six modules: (a) Screening, (b) Attention, (c) Language, (d) Memory, (e) Spatial, and (f) Executive Functions. The Screening Module is an abbreviated version of the full NAB. The purpose of this descriptive study was to present index and primary test score information for the Screening Module in a mixed sample of patients with known neurological conditions. Participants were 37 outpatients with clear evidence of neurological damage or disease. Performance decrements were found on the Attention Index, most notably on the Numbers and Letters tests. Decrements were also found on the Executive Functions Index, most notably on the Word Generation test. Somewhat surprisingly, patients performed well across most of the individual test scores. This mixed clinical sample showed less neuropsychological compromise than the clinical samples presented in the NAB manual.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".