Association between pulmonary ventilatory function and mild cognitive impairment: A population-based study in rural China
Bibliographic record
Abstract
Background Mild cognitive impairment (MCI), a reversible intermediate state, plays an important role in the development and prevention of dementia. The relationship between pulmonary function and MCI risk has not yet been well-elucidated. Methods We included 2,947 rural Chinese residents aged ≥35 years who were free from a history of stroke, dementia, or other brain diseases and measured pulmonary ventilatory function using calibrated spirometry according to the recommended method. MCI was assessed with the Montreal Cognitive Assessment-Basic for Chinese scale. Logistic regression models and restricted cubic splines with covariate adjustment were performed to explore the association between pulmonary function and MCI risk. Results The prevalence of MCI increased with decreasing pulmonary function, from the lowest quartile to the highest quartile of pulmonary function: 63.9, 50.5, 43.8, and 43.6%, respectively. After adjustment for confounding factors, participants in the first quartile had a significantly increased risk of MCI (ORs, 1.691, 95% CI, 1.267–2.258), with the highest quartile as the reference. In the subgroup analysis, a significant association of pulmonary function and MCI was found in females and those with low physical activity. Meanwhile, we observed an L-shaped relationship between pulmonary function and MCI (Pnon−linear = 0.032). Conclusions Poor pulmonary function was associated with an increased risk of MCI among rural Chinese adults, and presented a non-linear relationship. These findings remind us of the need for early cognitive assessment in local populations with lower pulmonary function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".