The Diagnostic Utility of the NINDS-CSN Neuropsychological Battery in Memory Clinics
Bibliographic record
Abstract
AIMS: To examine the diagnostic utility of the National Institute of Neurological Disorders and Stroke and the Canadian Stroke Network (NINDS-CSN) neuropsychological battery in memory clinics comparing controls with patients with no cognitive impairment (NCI), patients with cognitive impairment-no dementia (CIND) at varying severity levels (mild/moderate), and patients with dementia. METHODS: A total of 405 participants with NCI, CIND or dementia were assessed with the NINDS-CSN battery. The discriminatory properties of all three protocols (5, 30 and 60 min) before and after education stratification (none/primary vs. secondary/above) were examined by receiver operating characteristic curves. RESULTS: Overall, the shorter protocols are equivalent to the longer protocol in diagnosing dementia, regardless of education. To discriminate between nondementia groups, before education stratification, the 5-min protocol showed varied discriminatory properties between different diagnostic/severity groups. After stratification, the 5-min protocol was broadly equivalent to the longer protocols in lower-education groups [area under the curve (AUC) range: 0.77-0.87] but was less accurate in the higher-education groups (AUC range: 0.68-0.78). The 30- and 60-min protocol constantly showed moderate-to-excellent differentiating capacities regardless of education (AUC range: 0.80-0.90). CONCLUSION: The NINDS-CSN neuropsychological battery can be applied in memory clinics and effectively discriminate between cognitively intact individuals and those with cognitive impairments of varying severity. Furthermore, level of education should be taken into consideration when choosing protocols with different lengths for cognitive assessment.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".