Feasibility Analysis of the Computer-aided Language Assessment System in Measuring Cognitive-linguistic Impairment
Bibliographic record
Abstract
Background Cognitive impairment prevalence is increasing as aging population grows in China, which greatly affects the quality of life of the sufferers. Currently, the screening forcognitive-linguistic impairment still relies on traditional neuropsychological scales, which are technically demanding, time-consuming, and poorly tolerant. Objective To explore the feasibility of the Computer-aided Language Assessment System (CLAS) in the measurement of cognitive-linguistic impairment. Methods Random sampling method was used to recruit 73 participants, among them 55 (75.3%) were stroke/brain injury patients〔with a baseline score of 10-20 on the Mini-Mental State Examination (MMSE) 〕hospitalized in Department of Rehabilitation Medicine, the First Hospital of Jinan University from March 2018 to March 2020, and the other 18 (24.7%) were healthy volunteers (consisting of undergraduate medicalinterns from Jinan University, family members and accompanying caregivers of the patients) . The CLAS, Montreal Cognitive Assessment Scale (MoCA) , MMSE and Aphasia Battery of Chinese (ABC) were used to evaluate the linguistic and cognition functions of the participants. The Spearman correlation was used to assess the correlation of the score of CLAS with that of MoCA and MMSE. A receiver operating characteristic curve (ROC) of CLAS was plotted to estimate its diagnostic value for cognitive-linguistic impairment, with sensitivity, specificity and accuracy being calculated as well. A satisfaction survey was conducted in 18 healthy volunteers to understand their satisfaction with the use of the CLAS. Results The total CLAS score was positively correlated with that of MMSE, and MoCA (rs=0.910, 0.884, P<0.05) .Compared with MoCA (total MoCA score <26) in combination with ABC in diagnosing cognitive impairment, the CLAS had an AUC of 0.733〔95%CI (0.632, 0.834) , P<0.001〕in identifying cognitive-linguistic impairment when the optimal cut-off value was set as 85 points, and the maximum Youden index was obtained, with 1.000 sensitivity, 0.703 specificity, and 0.931 (68/73) accuracy. The average satisfaction score of 18 healthy volunteers was (4.07±0.48) , indicating an overall satisfaction level of "satisfactory". Conclusion High participant satisfaction with the CLAS was obtained in this study. And as the CLAS has proven to have good validity and diagnostic accuracy, as well as good performance in identifying cognitive-linguistic impairment, it could be applied to the screening and identification of cognitive-linguistic impairment.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".