Effect of intracutaneous pyonex on analgesia and sedation in critically ill patients with mechanical ventilation
Bibliographic record
Abstract
The purpose of this study was to evaluate the effect of intracutaneous pyonex on analgesia and sedation in critically ill patients who underwent mechanical ventilation. A total of 88 critically ill patients were divided into a control group and an intervention group. Critical Care Pain Observation Tool (CPOT) and Richmond Agitation and Sedation Scale (RASS) were used to evaluate pain and agitation. The dosage and treatment period of sedative and analgesic drugs in the intervention group were notably lower than the control group (p < 0.05). Analgesia compliance time in the intervention group was superior to control group (p < 0.05). The shallow sedation compliance rate in the intervention group was significantly higher than the control group (p < 0.01). There was significant difference in blood gas analysis before and after treatment between the two groups (p < 0.05). After 2 h of sedation and analgesia, heart rate in the intervention group was lower than control group, but respiratory rate was higher than the control group (p < 0.05). The traditional analgesia and sedation combined with intracutaneous pyonex reduced the total amount and treatment period of sedative and analgesic drugs in critically ill patients throughout the treatment process, and it also decreased the adverse reactions such as blood pressure drops and respiratory depression.
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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) | 0.001 | 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".