Assessment of Cognitive Dysfunction and Quality of Recovery after General Anesthesia in Perimenopausal Women
Bibliographic record
Abstract
Background: Perimenopausal women often experience neurocognitive fluctuations due to hormonal changes, which may predispose them to cognitive dysfunction following general anesthesia. The perioperative period presents a unique vulnerability to such patients, yet limited data exist on their cognitive outcomes and overall recovery quality. Methods: This prospective observational study included 120 perimenopausal women (age 45–55 years) scheduled for elective surgery under general anesthesia. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) preoperatively, on postoperative day 1 and day 7. Quality of recovery was evaluated using the QoR-15 scale. Descriptive and inferential statistics were applied, including paired t -tests and ANOVA. Results: On postoperative day 1, the mean MMSE score decreased significantly from 28.7 ± 1.1 to 26.3 ± 1.6 ( P < 0.001), while the MoCA score dropped from 25.2 ± 1.5 to 22.0 ± 2.0 ( P < 0.001). Both scores showed improvement by day 7 (MMSE: 27.9 ± 1.3, MoCA: 24.1 ± 1.7) but remained below baseline ( P < 0.05). The mean QoR-15 score was 112.4 ± 9.8 on day 1, significantly lower than the preoperative value of 131.6 ± 6.5 ( P < 0.001), with partial recovery noted on day 7 (124.9 ± 8.2). Conclusion: Perimenopausal women exhibit significant early postoperative cognitive dysfunction and impaired quality of recovery after general anesthesia. Though partial cognitive and functional recovery occurs within a week, baseline levels are not fully restored.
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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 | 0.000 |
| 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".