Normative study of SATURN: a digital, self-administered, open-source cognitive assessment tool for Italians aged 50–80
Bibliographic record
Abstract
Introduction: This study aimed to establish normative data for the Self-Administered Tasks Uncovering Risk of Neurodegeneration (SATURN), a brief computer-based test for global cognitive assessment through accuracy and response times on tasks related to memory, attention, temporal orientation, visuo-constructional abilities, math (calculation), executive functions, and reading speed. Methods: A sample of 323 Italian individuals with Montreal Cognitive Assessment (MoCA) equivalent score ≥1 (180 females; average age: 61.33 years; average education: 11.32 years), stratified by age, education, and sex, completed SATURN using PsychoPy, and a paper-and-pencil protocol consisting of Mini-Mental State Examination (MMSE) and MoCA. Data analyses included: (i) correlations between the total accuracy scores of SATURN and those of MMSE and MoCA; (ii) multiple regressions to determine the impact of sex, age, and education, along with the computation of adjusted scores; (iii) the calculation of inner and outer tolerance limits, equivalent scores, and the development of correction grids. Results: The mean total time on tasks was 6.72 ± 3.24 min. Age and education significantly influence the SATURN total accuracy, while sex influences the total time on tasks. Specific sociodemographic characteristics influence subdomain accuracies and times on task differently. For the adjusted SATURN total score, the outer limit corresponds to 16.56 out of 29.00 (cut-off), while the inner limit is 18.57. SATURN significantly correlates with MMSE and MoCA. Discussion: In conclusion, SATURN is the first open-source digital tool for initial cognitive assessment in Italy, showing potential for self-administration in primary care, and remote administration. Future studies need to assess its sensitivity and specificity in detecting pathological cognitive decline.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".