Clinical case of postoperative anesthesia of a patient by using subanesthetic dose of ketamine in severe abdominal pathology
Bibliographic record
Abstract
Pain is an inevitable consequence of surgical interventions in children, resulting in great stress and discomfort not only for patients but also for their parents. The intensity of the pain depends not only on the level of injury after the operation, but also on the localization and the nature of the procedure. Management of pain in children is best done through a multimodal approach: opioids, auxiliary drugs such as nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen, anti-neuroleptics such as gabapentin, and regional anesthetic methods. Postoperative anesthesia in abdominal surgery at present is a topical problem in anesthetic practice. In this clinical case, we would like to demonstrate the experience of applying post-operative anesthesia using subnormal dosages of ketamine. The patient was given anesthesia with prolonged infusion of a ketamine solution in a submorbid dose of 0.2 mg/kg/h IV. An assessment of the quality of anesthesia by assessing the level of stress markers, such as blood glucose, cortisol levels, and the assessment of the pain level on the NIPS scale was performed. Conclusion: The use of a ketamine solution in a dose of 0.2 mg/kg/h has a positive effect on treating postoperative pain in patients after severe abdominal surgical interventions. Applying a ketamine solution in a dose of 0.2 mg/kg/h reduces tolerance of the patient to opioid analgesics and the development of hyperalgesia and allodynia.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".