Supplementary Material for: High Prevalence of Mild Cognitive Impairment in the Elderly: A Community-Based Study in Four Cities of the Hebei Province, China
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) has been suggested as a term for a boundary area between normal aging and dementia. This study was designed to determine the prevalence of MCI in the elderly in the Hebei province, China, and explore its related factors. Methods: Participants included 2,601 community-dwelling people aged 60 years or older who resided in the four major cities of the Hebei province. In stage 1 of the study, the Mini-Mental State Examination and the Montreal Cognitive Assessment were administered for screening purposes. In stage 2, the subjects who screened positive were further examined by neurologists. The diagnosis of MCI was made according to Petersen's criteria. Results: The estimated prevalence of MCI was 21.3%. MCI was more prevalent at age 65-69 (28.3%), and its overall rates among men (24.1%) were higher than those of women (19.9%). The higher prevalence of MCI was associated with very old age (≥80 years old; OR = 2.457, 95% CI = 1.471-4.104), male gender (OR = 1.363, 95% CI = 1.097-1.694), low education level (OR = 2.439, 95% CI = 1.623-3.663), and poor economic status (OR = 2.882, 95% CI = 1.949-4.255). Conclusions: Our findings show a high prevalence of MCI in the elderly urban population in the Hebei province. Gender, education level, and economic status may have an important role in the etiology of MCI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.401 | 0.038 |
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".