Analysis the Safety and Efficacy at Different Types of Anesthesiological Support During Aesthetic Interventions on the Breast Glands in Ukraine
Bibliographic record
Abstract
The aim is to learn the features of aesthetic and reduction surgical interventions on the mammary glands in Ukraine. Materials and methods. The study was conducted by analyzing the inpatient ambulatory cards of 320 patients. Anesthesia was provided by propofol (n=130), sevoflurane (n=140) and combined use of sevoflurane and nalbuphine (n=50). The results of the study. It was found that usage of combined inhalation analgesia of sevoflurane with opioids was characterized by 41.9% less recovery time. It was found that 8 hours after surgery, the individual assessment of pain was lower in the group of combined analgesia with opioids relative to intravenous anesthesia with propofol (87.5%, p<0.05) and inhalation anesthesia with sevoflurane (71, 3%, p<0.05). After 24 hours all patients reported about pain below 1.0 point, however, in groups where sevoflurane and nalbuphine were used, the level of pain self-esteem was 2.61 and 3 times lower than after intravenous propofol. It was found that within 1 hour after surgery, the average cognitive score on the Montreal scale decreased in the group of intravenous propofol by 5.0% (p<0.05) and by 1.7% under inhalation anesthesia with sevoflurane. Under combined anesthesia the cognitive score remained at 12.0 points. The frequency of postoperative nausea was the highest level in the group of inhalation anesthesia - 16.7%. The addition of nalbuphine to sevoflurane significantly reduced the risk of postoperative nausea (χ2=7.250; p=0.007). Conclusions. Combined anesthesia with opioids is a highly effective anesthetic choice for aesthetic and reconstructive interventions on the mammary glands.
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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 | 0.000 | 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) | 0.000 | 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".