Rate of conversion from mild cognitive impairment to dementia in a Thai hospital‐based population: A retrospective cohort
Bibliographic record
Abstract
Abstract Introduction Mild cognitive impairment (MCI) is the state between normal cognition and dementia. This study objective was to estimate an average 1‐year rate of conversion from MCI to dementia and explore the associated factors of conversion in a hospital‐based cohort. Methods A retrospective cohort study of participants with MCI was conducted in a tertiary care hospital in Thailand. Two hundred fifty participants, 50 years of age or older, were enrolled. Results An average 1‐year conversion rate from MCI to dementia was 18.4%. MCI patients who converted to dementia were likely older ( P < .001), predominantly female ( P = .028), vitamin D deficient ( P = .012), and associated with lower Mini–Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores during first assessments ( P < .001, P < .001 respectively) and follow‐up assessments ( P < .045, P < .001 respectively). We conducted two models of multivariate analysis, using binary logistic regression. In the first model, adjusted for age, sex, education, vitamin D deficiency, and first assessment MMSE scores, we found that underlying vitamin D deficiency (odds ratio [OR] = 3.13, 95% confidence interval [CI] 1.04 to 9.44) and first assessment MMSE scores (OR = 0.83, 95% CI 0.73 to 0.93) were significantly associated with conversion to dementia. In the second model, adjusted for age, sex, education, vitamin D deficiency and first assessment MoCA scores, only first assessment MoCA scores (OR = 0.58, 95% CI 0.45 to 0.76) were significantly associated with conversion to dementia. Discussion The 1‐year conversion rate from MCI to dementia was 18.4%. MMSE and MoCA were useful tools to assess baseline cognitive status in MCI patients and predict dementia progression. The association between vitamin D deficiency and risk of conversion from MCI to dementia requires further investigations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".