Validation of the Non-Language-Based Cognitive Assessment
Bibliographic record
Abstract
Objective To develop the Non-Language-Based Cognitive Assessment （NLCA） appficable to patients with aphasia and to validate the reliability and vafidity of NLCA. Methods Seventy-three normal subjects and 32 patients with mild cognitive impairment were evaluated by the NLCA and the Mini-Mental State Examination. Forty subjects were randomly selected from the normal subject samples were assessed with the NLCA, the Auditory Verbal Learning Test, the Rey-Osterrieth Complex Figure Test （Form A）, the Stroop Color-Word Interference Test, the Raven＇s Standard Progressive Matrices （Part A）, and the WAIS Digit Span Test. Results The NLCA had high inter-rater agreement （Cronbach＇s α coefficient 0. 836）, reliability among the assessors 0. 895 - 0. 953, test-retest reliability 0. 863 - 0. 952 at at a 2-6 week interval. The years of education was significantly correlated with NLCA （r = 0. 852, r 〈 0. 01）. When the Montreal Cognitive Assessment was used as diagnostic criteria, the area under the receiver operating characteristic curve was 0. 899 （95% confidence interval 0. 827 - 0. 972）. When cutoff at 70, the NLCA had had high sensitivity, specificity, positive predictive value and negative predictive value in the identification of patients with mild cognitive impairment. Conclusions The NLCA has good reliability and validity. It is an effective cognitive function assessment that meets the basic requirements of the neuropsychological tests. Key words: Cognition Disorders; Neuropsychological Tests; Aphasia
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".