Healthy Older Adult Performance on A Modified Version of the Cognistat (NCSE): Demographic Issues and Preliminary Normative Data
Bibliographic record
Abstract
Normative data for a healthy sample of older adults (n = 108) ranging in age from 60 to 96 are provided for the Cognistat, a mental status exam previously known as the Neurobehavioral Cognitive Status Examination (NCSE). A Cognistat Composite Score is also introduced that is intended to be used as a marker of general cognitive impairment, allowing the Cognistat to be used to match patients in terms of the severity of their cognitive dysfunction. The "screen and metric" approach of the Cognistat was abandoned in order to improve the reliability and standardization of this measure by administering the entire metric to all patients. The impact of demographic variables on Cognistat performance was examined, demonstrating that both age and education contribute uniquely to a number of Cognistat subtests as well as to the Cognistat Composite Score. This study highlights the importance of matching an examinee's demographic background to the normative sample with which his or her test score is being compared. Normative data were stratified accordingly by age and by both age and education. Current results indicate that the Cognistat is sensitive to normal aging and promises greater sensitivity to the impact of age than the commonly employed Mini-Mental State Examination (MMSE).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".