COGNITIVE DYSFUNCTION AS A FACTOR RESTRICTING THE APPLICATION OF ACCELERATED REHABILITATION PROGRAMS FOR ELDERLY AND SENIOR PATIENTS IN OPERATIVE GYNECOLOGY
Bibliographic record
Abstract
Aim: to identify risk groups for the formation of cognitive dysfunction in elderly patients after gynecological operations. Research base: City Clinical Hospital named after S.P. Botkin, Moscow, Russian Medical Academy of Continuing Professional Education. The risk groups for the formation of cognitive dysfunction in elderly and senior patients after gynecological operations are determined. Hospitalization was carried out just before the operation, the postoperative hospital bed rest varied from 2 to 4 days. Where possible, the principles of FastTrack technology were applied in the perioperative period. Materials and methods. The study involved 60 elderly and senior female patients after menopause from the age of 50 to 85 years (75,2 ± 6,3 years), who underwent surgery under conditions of endotracheal anesthesia due to benign pathology (ovarian neoplasms, genital prolapse). The principles of FastTrack – an accelerated path in surgery were applied. Before the operation and on the 2nd day after the operation, testing was performed on the MMSE (Mini-mental State Examination) scale, as well as on the questionnaire to identify Spielberger-Hanin personal and reactive anxiety. Results. It has been proven that the frequency of postoperative cognitive dysfunction increases in proportion to the age of the patients and often depends on the type of anesthetic benefit and the severity of the operation. In the group of women aged from 50 to 60, no initial cognitive impairment was found, 18 % of patients after 60 years of age have cognitive impairment, and 5 % have mild dementia. The older the patient, the more cases of postoperative cognitive impairment develops/worsens, and high levels of anxiety remain. Early discharge in such cases can cause problems. Conclusion. In risk groups, especially in patients older than 70 years with signs of widespread atherosclerosis, cerebrovascular accidents, and a history of brain injuries, it is necessary to accurately identify signs of cognitive impairment and promptly involve a neurologist and clinical psychologist to correct them, which will significantly improve the postoperative period and reduce the anxiety level
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".