Beyond the Diagnosis: Identifying Major Risk Factors for Dementia in a Clinical Setting
Bibliographic record
Abstract
Background: Dementia is a serious health issue, and effective management requires an understanding of its risk factors. The purpose of this study was to assess dementia risk factors in patients from Bolan Medical Complex Hospital, Quetta. Methods: From April 2021 to April 2024, a cross-sectional study was carried out with participants aged 18 and older who had been diagnosed with dementia using DSM-5 criteria. Demographic and risk factor-related data were collected through structured interviews, and cognitive status was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Results: The study included 178 patients, selected from a convenience sample. We found that two important independent risk factors for dementia were stroke (p < 0.001) and Wilson’s disease (p < 0.001). Significant correlations were observed between other dementia subtypes and stroke (OR = 0.339, 95% CI: 0.195 - 0.583) and Wilson’s Disease (OR = 0.424, 95% CI: 0.297 - 0.606). After adjusting for confounding factors, no additional variables were significantly associated with the risk of dementia, including age, gender, urbanization, socioeconomic status, diabetes, thyroid status, hypertension, family history, B12 deficiency, cardiovascular diseases, smoking, alcohol use, or physical activity. Conclusion: It has been determined that stroke and Wilson’s disease are significant risk factors for dementia, especially the group of dementias other than Alzheimer’s and vascular dementia. According to these results, reducing the risk of dementia may benefit from focused screening and intervention for those with a history of stroke and Wilson’s disease. Additional longitudinal research is required to validate these correlations and investigate other risk factors.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".