THE ROLE OF COGNITIVE RESERVE IN PREDICTING COGNITIVE EFFICIENCY
Bibliographic record
Abstract
Objectives. The objective of the study is to assess cognitive reserve and to investigate the role of age and educational instruction level in cognitive efficiency. Material and methods. All 146 participants, 105 women (72%), 41 men (28%), aged 60-96 years (M = 74.61, SD = 7.12), with primary to postgraduate studies (M = 3.08, SD = 1.54) completed the following test battery: questionnaire "Cognitive Reserve Index" (R-IRCq), Minimal Assessment of Cognitive Status-2 (MMSE-2) and Montreal Cognitive Assessment (MoCA). Results. The educational level as well as the total cognitive reserve index are significant predictors of cognitive efficiency measures. Age and total R-IRCq score cover 32% of MoCA variance. Age and educational level cover 36% of the MoCA variance (adjusted R² = 0.36, F(2.143) = 42.05, p < .001), age (B = - 0.08, β = - 0.27, t = - 3.77) and educational level (B = 0.62, β = 0.43, t = 5.90). Conclusions. An inverse correlation between age and cognitive efficiency has been identified: the older the age of participants, the lower the cognitive efficiency, the stronger the correlation when evaluated by MoCA. Both educational levels and total R-IRCq index partially mediated the effect of age on cognitive performance (MoCA). The assessment of cognitive reserve in older people could be a useful additional measure to integrate existing protocols for the neuropsychological assessment of cognitive decline. Cognitive reserve should also be recognized as a factor, which will influence the rate of cognitive decline after diagnosis. Keywords: MMSE-2, MoCA, cognitive reserve, education, 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".