Reply to: Admission of very old patients with respiratory infections to intensive care units: should we consider the sex in the decision-making process?

<title>Extract</title> We thank A. Guillon and co-workers for their valuable interest in our work [1], and for underscoring an important issue regarding the role of sex and gender in respiratory medicine. They identified and validated an artificial intelligence (AI)-based strategy to prognosticate outcomes in elderly patients admitted to the intensive care unit (ICU) for respiratory tract infections. Their predictive framework evaluated various machine learning (ML) algorithms, a branch of AI in clinical research, highlighting the superior performance of logistic regression (area under the curve of 0.70 (95% CI 0.69–0.72)). Male sex was identified as a significant risk factor for mortality within the cohort. Although the overall predictive accuracy was modest, as acknowledged by the authors, confirmatory analyses using SHapley Additive exPlanations (SHAP) and variable importance from permutation (VIMP) affirmed the robust contribution of sex to mortality risk stratification. Notably, recent reviews in respiratory medicine have underscored the critical importance of integrating ML into prognostic modelling in a way that preserves interpretability and relevance to patient stratification [2].