C-71Cognitive Screener versus the Montreal Cognitive Assessment
Bibliographic record
Abstract
Objective: This poster compares the Cognitive Screener (CS) vs the Montreal Cognitive Assessment (MoCA) as quick screening measures of neuropsychological impairment. Method: Thirty-seven adult patients referred by neurologists and psychiatrists for outpatient neuropsychological evaluation at a private practice office were administered full neuropsychological batteries that included the CS, MoCA, Reynolds Intellectual Assessment Scale (RIAS) Composite Intelligence Index (CIX) and several tests used to calculate the Alternative Impairment Index (AII), a summary measure of neuropsychological impairment. The CS is the mean of two subtests of the Test of Verbal Conceptualization and Fluency (TVCF) the Letter Naming (LN) and Trails C (TC). The patients included 22 females, 29 Caucasians, 5 African-American.2 Asian-American and 1 Hispanic/Latino-American. 33 patients were right handed. Diagnoses include Stroke-17, Head Injury-12, Alzheimer's disease-2 Epilepsy-1, Multiple Sclerosis-1, Brain Tumor-1, Anoxic Brain Damage-1 and Attention Deficit Hyperactivity Disorder-1. Ages ranged from 21-90 (Mean-58.8, Standard Deviation-14.24) and education ranged from 9-18 years (Mean-14.5, Standard Deviation-2.5). All subjects had signed informed consent documents. Results: The variables of CS and MoCA were each correlated with each other and the CIX and AII. Results: The correlations between the CS and MoCA (.627), CIX (.386), and AII (-.574) and MoCA and the CIX (.592) and AII (-.706) were all statistically significant at the P < .01 level. Conclusion: The CS shows promise similar to the MoCA as a quick screening measure of neuropsychological impairment but is quicker to administer. Further research on the CS appears warranted.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".