Computerized Cognitive Screen (CoCoSc): A Self-Administered Computerized Test for Screening for Cognitive Impairment in Community Social Centers
Bibliographic record
Abstract
BACKGROUND: Computerized cognitive tests may serve as a preliminary, low-cost method to identify individuals with suspected cognitive impairment in the community. OBJECTIVE: To develop a self-administered computerized test, namely the "Computerized Cognitive Screen (CoCoSc), Hong Kong version", for screening of individuals with cognitive impairment (CI) in community settings. METHODS: The CoCoSc is a 15-min computerized cognitive screen covering memory, executive functions, orientation, attention and working memory, and prospective memory administered on a touchscreen computer. Individuals with CI and cognitively normal controls were administered the CoCoSc and the Montreal Cognitive Assessment (MoCA). Validity of the CoCoSc was assessed based on the relationship with the MoCA using Pearson correlation. Receiver operating characteristic curve (ROC) was used to examine the ability of the CoCoSc to differentiate CI from controls. RESULTS: Fifty-nine individuals with CI and 101 controls were recruited. Seventy-five (46.9%) participants had ≤6 years of education. Performance on the CoCoSc differed between normal and CI groups in both low and high education subgroups. Total scores of the CoCoSc and MoCA were significantly correlated (r = 0.71, p < 0.001). The area under ROC was 0.78, p < 0.001 for the CoCoSc total score in differentiating the CI group from the cognitively normal group. A cut-off of ≤30 on the CoCoSc was associated with a sensitivity of 0.78 and specificity of 0.69. The CoCoSc was well accepted by attendees of community social centers. CONCLUSION: The CoCoSc is a promising computerized cognitive screen for self-administration in community social centers. It is feasible for testing individuals with high or low education levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".