The Colorado Cognitive Assessment (CoCA): Development of an Advanced Test of Cognitive Status
Bibliographic record
Abstract
Abstract Objective Current brief tests of cognition provide a rough indication of overall cognitive functioning that assist with making gross clinical judgments (e.g., demented vs. not demented). The purpose of the present study was to develop and preliminarily validate a brief tool, The Colorado Cognitive Assessment (CoCA) to facilitate early and accurate diagnosis of mild and atypical presentations of dementia. A related goal was to compare its psychometric properties with The Montreal Cognitive Assessment (MoCA). Method Participants were 150 community dwelling adults over the age of 50 without a known mental health or neurological condition. Confirmatory factor analysis (CFA) was used to assess model fit of the CoCA and MoCA. Measurement invariance (MI) was evaluated using the multiple-indicators multiple-causes modeling (MIMIC) approach. Results The CFA model of the CoCA revealed excellent fit; χ2(44) = 47.506, p = .332; CFI = 0.987; TLI = 0.983; RMSEA = 0.023 (90% CI [0.000, 0.061]); SRMR = 0.048. MI analyses revealed that items on the CoCA were invariant to sex, age, education, and mood. In comparison, a CFA model of the MoCA had worse fit; χ2(14) = 28.536, p = .012; CFI = 0.817, TLI = 0.725; RMSEA = 0.083 (90% CI [0.038, 0.127]); SRMR = 0.062; and was biased by age, education, and depressive symptomatology. The global factor score reliability of the CoCA (r = .84) was better than the MoCA (r = .74). Conclusion Results provide preliminary evidence for the CoCA as a reliable and comprehensive cognitive instrument with further cross-sectional and longitudinal research needed for its validation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".