Development and Standardization of a New Cognitive Assessment Test Battery for Chinese Aphasic Patients: A Preliminary Study.
Bibliographic record
Abstract
BACKGROUND: Nonlinguistic cognitive impairment has become an important issue for aphasic patients, but currently there are few neuropsychological cognitive assessment tests for it. To get more information on cognitive impairment of aphasic patients, this study aimed to develop a new cognitive assessment test battery for aphasic patients, the Non-language-based Cognitive Assessment (NLCA), and evaluate its utility in Chinese-speaking patients with aphasia. METHODS: The NLCA consists of five nonverbal tests, which could assess five nonlinguistic cognitive domains such as visuospatial functions, attention test, memory, reasoning, and executive functions of aphasic patients. All tests are modified from the nonverbal items of the current existed tests with some changes to the characteristics of Chinese culture. The NLCA was tested in 157 participants (including 57 aphasic patients, 50 mild cognitive impairment (MCI) patients, and 50 normal controls), and was compared with other well-established relative neuropsychological tests on the reliability, validity, and utility. RESULTS: The NLCA was fully applicable in the MCI patients and the normal controls, almost working in the aphasic patients (57/62 patients, 91.9%). The NLCA scores were 66.70 ± 6.30, 48.67 ± 15.04, and 77.58 ± 2.56 for the MCI group, the aphasic group, and the control group, respectively , and a significant difference was found among three groups (F = 118.446, P < 0.001). The Cronbach's alpha of the NLCA as an index of internal consistency was 0.805, and the test-retest and interrater reliability was adequate (r=0.977 and r= 0.970, respectively). The correlations of the cognitive subtests and their validation instruments were between 0.540 and 0.670 (all P < 0.05). Spearman's correlation analysis indicated that the coefficient of internal consistency of each subtest itself was higher than other subtests. When choosing the Montreal Cognitive Assessment score of <26 as the diagnostic criteria of cognitive impairment, the area under the curve for all participants in the control and MCI groups was 0.942 (95% confidence interval: 0.895-0.989), and an optimal cutoff point of 75.00 seemed to provide the best balance between sensitivity and specificity. Age (r = -0.406, P < 0.001) was the main influence factor for the NLCA. CONCLUSIONS: The NLCA could efficiently differentiate the cognitive impairment patients from the normal controls and is a reliable and valid cognitive assessment test battery to specially find nonlinguistic cognitive function for aphasic patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".