COMPARISON OF GENERAL AND SPINAL ANESTHESIA IN TERMS OF POSTOPERATIVE COGNITIVE DECLINE USING THE MMSE AND MOCA AFTER MINOR ELECTIVE SURGERY IN ELDERLY PATIENTS
Bibliographic record
Abstract
Introduction and aim Postoperative cognitive dysfunction is an important complication associated with increased morbidity, mortality, and reduced quality of life. Generally studies have focused on major surgery, while there is little evidence of the incidence of cognitive dysfunction in minor surgery. We aimed to compare general and spinal anesthesia in terms of cognitive decline in elderly patients after elective minor surgery using the Mini-mental state examination and Montreal cognitive assessment. Material and methods This observational study was conducted June 2014 to March 2015 at Ankara Numune Education and Research Hospital. The Mini-mental state examination and Montreal cognitive assessment scores were evaluated before and one day after the operation. Results The postoperative Mini-mental state examination scores of patients (26.23±2.77) were significantly lower than the preoperative scores (27.17±1.93) only in the general anesthesia group (p =0.003), while the postoperative Montreal cognitive assessment scores (22.87±3.88 for general and 23.13±4.08 for spinal anesthesia) were lower than the preoperative scores (24.32±3.19 for general and 24.35±2.84 for spinal anesthesia) in both the general and spinal anesthesia groups (p =0.000 and 0.019, respectively). The Postoperative cognitive dysfunction incidence was 32.9% using the Montreal cognitive assessment and was not significantly different between anesthesia methods. Conclusion Early Postoperative cognitive dysfunction is an important problem after elective minor surgeries, even with spinal anesthesia, in elderly patients. The Montreal cognitive assessment is an alternative tool that can be applied in a short time for screening cognitive functions in elderly patients. The cognitive screening of elderly patients perioperatively may be beneficial.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".