Impacto de um protocolo de analgesia multimodal em unidade de terapia intensiva: estudo de coortes antes e depois
Bibliographic record
Abstract
Introduction: Opioids are the basis for pain treatment in critically ill patients.However, recent attention to its adverse effects in the intensive care unit (ICU) has led to the use of strategies aimed at reducing its side effects.Among these strategies there are multimodal analgesia protocols, which prioritize pain management and employ a combination of different analgesics to spare excessive doses of opioids and sedatives in continuous infusion.Objective: To evaluate the impact of a multimodal analgesia protocol on clinical outcomes and consumption of sedatives and analgesics in two Intensive Care Units.Methods: We conducted a singlecenter, quasi-experimental, retrospective and prospective cohort study comparing clinical outcomes and consumption of sedatives and analgesics before and after the implementation of a multimodal pain management protocol in critically ill adult patients.We included 465 patients in 2017 (pre-intervention group) and 1508 between 2018 and 2020 (post-intervention group).Results: In the analysis of the primary outcome there was a significant decrease in mortality between the years 2017 and 2020 (27.7% -21.7%, p=0.0134).There was no statistical difference in mechanical ventilation time, mobility rates, infection rate and incidence of tracheostomies.Patients who received the multimodal analgesia protocol had a decrease in mean fentanyl intake of 24% and a progressive decrease in oral morphine equivalent (OME) consumption (8.4 -19%).There was an increasing trend in the use of adjuvant analgesics and morphine in preemptive and therapeutic analgesia.Conclusion: The implementation of a multimodal pain control protocol significantly reduced mortality and the use of opioids in ICU.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".