Validation of the Intelligent Cognitive Assessment System (ICAS) for stroke survivors
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To investigate the internal consistency, test-re-test reliability of an Intelligent Cognitive Assessment System (ICAS) and its optimal cut-off score for stroke patients with or without cognitive impairment. METHOD: A prospective cohort study design was adopted. Sixty-six post-stroke patients of aged 60 or above were recruited. They were screened by the Chinese version of Mini Mental State Examination (MMSE-CV) and assessed by the Intelligent Cognitive Assessment System (ICAS) consisting of 65 testing items which could be presented at a level according to stroke patient's response. It was administered to examine the internal consistency and test-re-test reliability (by repeating within a 7-day interval). The optimal cut-off score to screen stroke patients having cognitive impairment was determined by the receiver operating characteristics (ROC) curve. RESULTS: The internal consistency of the ICAS (Cronbach's alpha = 0.878) and its test-re-test reliability (rho = 0.789; p < 0.001) were demonstrated. The cut-off score for the ICAS to determine cognitive impairment was found to be 3.02, with a sensitivity of 80.5% and specificity of 96%. CONCLUSION: Preliminary results suggested that ICAS was a valid and reliable cognitive screening tool for stroke survivors. The ICAS can be further developed by studying its norms for stroke patients. It is also programmable for potential application to different countries by changing ICAS to other language versions and including other culturally relevant content.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".