Prospective association of general anesthesia with risk of cognitive decline in a Chinese elderly community population
Bibliographic record
Abstract
As life expectancy increases and the population grows, the number of surgeries performed each year is likely to continue to increase. We evaluated whether surgery with general anesthesia increases risk for cognitive impairment in a Chinese elderly community population. The current data was obtained from the China Longitudinal Aging Study (cohort 1) and Shanghai Brain Aging study (cohort 2). Cohort 1 included 1545 elderly people with normal cognitive function, who underwent a screening process that included physical examination, medical history, baseline and 1-year follow-up assessments of cognitive function by a face-to-face interview. Cohort 2 included an additional 194 elderly people with normal cognitive function, all of whom, unlike cohort 1, underwent T1-phase MR imaging scans. In cohort 1, 127 elderly people with normal cognitive function transformed into mild cognitive impairment, 27 into dementia, while 1391 still maintained normal cognitive function. By using Cox regression analysis, we found that surgery with general anesthesia was a risk factor for cognitive impairment (p = 0.013, HR = 1.506, 95% CI 1.091-2.078); In cohort 2, we found that elderly people with a history of surgery with general anesthesia had lower Montreal Cognitive Assessment (MoCA) scores and smaller right amygdala volume (p < 0.05). Through correlation analysis, we found that the volume of the right amygdala was significantly correlated (p = 0.003, r = 0.212) with MoCA. Then by using the linear regression analysis (mediation model), we found that surgery with general anesthesia directly affected the MoCA score by affecting the volume of the right amygdala (B = 1.315, p = 0.036 95% CI 0.088-2.542). We confirm surgery with general anesthesia as a risk factor for cognitive impairment, and its mechanism may be related to its effect on the volume of the right amygdala.
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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".