A comprehensive analysis of patient satisfaction with anesthesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Patient satisfaction with anesthesia after surgical treatment is a complex concept that includes not only the level of satisfaction with the anesthesia itself but also the presence of fears, worries, depression, evaluation of the anesthesiologists' work, as well as cognitive dysfunction as a possible negative consequence of anesthesia. Objective: Conducting a comprehensive analysis of patients' satisfaction with anesthesia. Methods: Questionnaire of patients' satisfaction with anesthesia (Sinbukhova E.V., Lubnin A.Yu.), State-Trait Anxiety Inventory in the adaptation by Y.L. Hanin, Assessment of Depression, The Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery. Population consisted of 202 patients. Results: Satisfaction with anesthesia: assessment “good and higher” with primary anesthesia – 59.7% of patients with repeated – 70% of patients. The most common factors that reduce the assessment of patients' satisfaction with anesthesia are: strong excitement before surgery about operation and anesthesia, no postoperative visit of the anesthesiologist, no visit of the anesthesiologist before the operation, not enough attention of anesthesiologist in the surgery room before anesthesia, nausea, vomiting, pain, dizziness, general discomfort, and thirst. MoCA cognitive assessment before and after anesthesia: P < 2.2 e–16 (significant decrease). Depression: major depression in 52% of patients, subclinical depression in 22.8%. Conclusion: Regular survey of patients' satisfaction should help to improve the quality of medical care. The strong excitement of the patient about the upcoming anesthesia and surgery, and the presence of a high level of anxiety and depression can be factors of reducing the patients' satisfaction with anesthesia. It requires psychological support of patients at the stage of surgical treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it