Testing whether cognitive reserve as measured by self-rating of stimulating activities moderates the association of polysubstance use and neurocognitive disorder
Bibliographic record
Abstract
Introduction: The objectives were to identify a latent factor of cognitive reserve (CR) assessed by self-rating of cognitively stimulating activities, to analyze the association between this factor and educational attainment, and to test whether CR moderates the association between polysubstance use and neurocognitive disorder (NCD). Methods: Cross-sectional data of 753 participants was collected in Mexico City. A questionnaire for self-rating of stimulating activities (work/education, leisure, physical, social, usual- and current environments) was designed. Confirmatory factor analysis was performed to test unifactoriality. This CR factor was then used within a structural equation model of moderation between recent- and years of substance use and indicators of NCD (Montreal Cognitive Assessment and an interview for subjective cognitive deficits). Results: We found acceptable goodness-of-fit values for the unifactorial model, but no association of this factor with educational attainment, nor with recent- and years of substance use (suggesting independence of CR and severity of neuropathology). We did not find a moderation effect of CR between substance use and indicators of NCD; CR was negatively associated with subjective cognitive deficits only. Conclusions: Moderation effect of self-rated CR should be further tested using direct measures of substance-induced neuropathology. Measurement of self-rated CR may complement self-reported cognitive examination.
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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 | 0.001 | 0.004 |
| 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) | 0.003 | 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".