Validation and Convergent Validity of the Boston Cognitive Assessment (BOCA) in an Italian Population: A Comparative Study with the Montreal Cognitive Assessment (MoCA) in Alzheimer’s Disease Spectrum
Bibliographic record
Abstract
Abstract Background The Boston Cognitive Assessment (BOCA) is a self-administered online test developed for cognitive screening and longitudinal monitoring of brain health in an aging population. The study aimed to validate BOCA in an Italian population and to investigate the convergent validity with the Montreal Cognitive Assessment (MOCA) in normal controls and subjects within the Alzheimer Disease spectrum. Methods BOCA was administered to 150 participants, including cognitively healthy controls (HC, n=50), patients with mild cognitive impairment (MCI, n=50), and dementia (DEM, n=50). The BOCA reliability was assessed using i) Spearman’s correlation analysis between subscales; ii) Cronbach’s alpha calculation, and iii) Principal Component Analysis. Repeated-measures ANOVA was employed to assess the impact of the sequence of test administrations between the groups. BOCA performance between HS, MCI and DEM were compared using Kruskall Wallis test. Furthermore, a comparison was conducted between MCI patients who tested positive for amyloid and those who tested negative, utilizing Mann Whitney’s U-test. Results Test scores were significantly different between patients and controls (p<0.001) suggesting good discriminative ability. The Cronbach’s alpha was 0.82 indicating a good internal consistency of the BOCA subscales and strong-to-moderate Spearman’s correlation coefficients between them. BOCA demonstrated strong correlation with Montreal Cognitive Assessment (MoCA) (rho=0.790, p<0.001). Conclusions The Italian version of the BOCA test exhibited validity, feasibility, and accurate discrimination.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".