The factors associated with mild cognitive impairment (MCI) in surgical menopause women.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: As a sizeable proportion of persons with mild cognitive impairment will progress to frank dementia, early detection is an important strategy to prevent and decelerate the progression of cognitive decline. In Thailand, the prevalence of mild cognitive impairment in surgical menopause women has not been well established. The objectives of the present study were to determine the percentage and factors associated with mild cognitive impairment in women with surgical menopause. MATERIAL AND METHOD: Between October 2013 and July 2014, 200 eligible women at King Chulalongkorn Memorial Hospital were enrolled. The self-reported questionnaires were used to obtain the demographic data and the Thai version of the Montreal Cognitive Assessment (MoCA) was used to detect mild cognitive impairment (MCI). The MCI was diagnosed when the MoCA score was less than 25. The data were statistically analyzed using SPSS version 17 for student t-test, Chi-square test, and multiple regression analysis. RESULTS: The percentage of MCI in the present study was 43.5%. The univariate analysis showed that factors significantly related to MCI were marital status, educational levels, occupation, monthly income, and duration of hormone replacement therapy (HRT). Nevertheless, multiple regression analysis revealed that only older age at enrollment, marital status, low educational level, and low monthly income were significantly related to MCI. CONCLUSION: Almost half of the surgical menopause women in the present study had MCI. Older age at enrollment, marital status, low educational level, and low monthly income were significantly related to MCI. Age at surgical menopause and HRT were not found to be associated with MCI in this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".