Cognitive reserve estimated with a life experience questionnaire outperforms education in predicting performance on MoCA: Italian normative data
Bibliographic record
Abstract
Abstract Normative data of neuropsychological tests typically take into account the effect of demographic variables like age and education on performance. However, a broad literature has shown that, after the school age, other cognitively stimulating experiences (e.g., occupational attainment and a variety of leisure-time activities) may increase and build up cognitive reserve (CR), which is positively associated with better performance in neuropsychological tests. With these premises, we investigated the predictive ability of education and a life-experience proxy of CR on a widely used cognitive screening, i.e., the Montreal Cognitive Assessment (MoCA). Results show that including the more comprehensive life-experience CR proxy is better than considering only education in predicting expected cognitive performance. Based on the results of our analyses we provide normative data and cut-offs on 440 Italian individuals aged 50-90 years, by taking into account, for the first time for the Italian population, a CR index, together with demographic variables and Education, in the calculation of regression-based norms. Accounting for life-experience CR proxies can improve the accuracy of normative data and allow a finer estimation of cognitive performance, which lead to a more tailored approach to patient assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".