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
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 healthy ageing population and patients 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 and within different severity subgroups 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 total and subscores differ across different MoCA severity subgroups and demonstrated strong correlation with MoCA scores (rho = 0.790, p < 0.001). CONCLUSIONS: The Italian version of the BOCA test exhibited validity, feasibility, and accurate discrimination closely performing as MoCA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".