Education and Environment Dementia Risk Factors: A Literature Review
Bibliographic record
Abstract
Introduction and Background: Dementia has many different causes. Dementia is considered a multifactorial disease; hence the interplay between factors for every case is a complex study. Early and recent reviews had recognised several risk factors associated with dementia in general and with AD specifically. The most studied risk factors include genetics, increasing age, education, environment, and brain injuries. This review aims to give readers access to the latest research on the educational and environmental risk factors of dementia by selecting recent high-quality resources and summarising them in this review. Methods: The current article is a narrative review of broad literature research. Results and Discussion: The comprehensive examination of evidence supports the following. First, low education can be considered a relevant risk factor for developing dementia, although the operationalisation of "low education" is still unclear in many studies. The mechanisms of "cognitive reserve" have an important implication in the relationships between education and dementia, and this has been studied with limitations in people with intellectual disorders. Second, air pollution is now considered a dementia risk factor with plenty of evidence concerning PM2.5 but less conclusive evidence regarding single gaseous pollutants because of the “multi-exposure response.” Conclusion: The considerable body of research points towards an association between these risk factors and dementia prevalence. Low- and middle-income countries will benefit from prioritising child education for all since education is one of the major risk factors for dementia and a wide variety of health disparities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".