The Colorado Cognitive Assessment (CoCA): Development of an Advanced Neuropsychological Screening Tool
Bibliographic record
Abstract
OBJECTIVE: The Colorado Cognitive Assessment (CoCA) was designed to improve upon existing screening tests in a number of ways, including enhanced psychometric properties and minimization of bias across diverse groups. This paper describes the initial validation study of the CoCA, which seeks to describe the test; demonstrate its construct validity; measurement invariance to age, education, sex, and mood symptoms; and compare it to the Montreal Cognitive Assessment (MoCA). METHOD: Participants included 151 older adults (MAge = 71.21, SD = 8.05) who were administered the CoCA, MoCA, Judgment test from the Neuropsychological Assessment Battery (NAB), 15-item version of the Geriatric Depression Scale (GDS-15), and 10-item version of the Geriatric Anxiety Scale (GAS-10). RESULTS: A single-factor confirmatory factor analysis model of the CoCA fit the data well, CFI = 0.955; RMSEA = 0.033. The CoCA factor score reliability was .84, compared to .74 for the MoCA. The CoCA had stronger disattenuated correlations with the MoCA (r = .79) and NAB Judgment (r = .47) and weaker correlations with the GDS-15 (r = -.36) and GAS-10 (r = -.15), supporting its construct validity. Finally, when analyzed using multiple-indicators, multiple-causes (MIMIC) modeling, the CoCA showed no evidence of measurement noninvariance, unlike the MoCA. CONCLUSIONS: These results provide initial evidence to suggest that the CoCA is a valid cognitive screening tool that offers numerous advantages over the MoCA, including superior psychometric properties and measurement noninvariance. Additional validation and normative studies are warranted.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".