ArticleFrontiers in medicine2026
Efficacy and safety of Oliceridine versus Sufentanil in postoperative analgesia for burn skin grafting: a machine learning and SHAP-based cohort study.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Balancing profound analgesia with the minimization of opioid-related adverse events, particularly postoperative nausea and vomiting (PONV), remains challenging in burn surgery. This study aimed to use machine learning to evaluate the comparative efficacy and safety of the novel biased agonist Oliceridine versus Sufentanil. Methods: A retrospective cohort of 586 patients undergoing burn skin grafting was analyzed. XGBoost algorithms were developed to predict the need for rescue analgesia (efficacy) and PONV occurrence (safety). The Shapley additive explanations (SHAP) were applied to decipher feature contributions and drug effects. Results: The XGBoost efficacy model (AUC = 0.843) identified total burn surface area and surgery duration as dominant predictors, whereas opioid choice had negligible SHAP impact, suggesting a comparable predictive analgesic profile between Oliceridine and Sufentanil in this cohort. Conversely, the XGBoost PONV model (AUC = 0.788) outperformed logistic regression, identifying Sufentanil administration and female patients as paramount risk drivers. SHAP interaction analysis revealed a strong predictive association in which substituting Sufentanil with Oliceridine correlated with a substantially blunted predicted PONV risk trajectory in female patients. Conclusion: Machine learning evaluation suggests a distinction between opioid predicted efficacy and safety profiles in this cohort. Oliceridine showed similar predicted analgesic outcomes to Sufentanil while being associated with a significantly lower predicted risk of PONV. These exploratory insights provide hypothesis-generating frameworks for risk stratification in severe burn management, though further prospective trials are required to establish any clinical validity.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.