Objective: To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. Methodology: A structured review was conducted following PRISMA 2020 guidelines. Thirty-one peer-reviewed studies were selected through a targeted manual search based on predefined eligibility criteria. Included studies evaluated functional recovery following TKA using validated outcome measures such as the WOMAC, KSS, KOOS, IKDC, SF-36, and SANE. Data extraction focused on the instruments used, patient population characteristics, and reported outcomes. A descriptive synthesis was compiled in Table 1. Additionally, 15 studies with quantitative data were analyzed using a forest plot to illustrate risk ratios (RR) and 95% confidence intervals (CI) for functional improvement. Risk of bias was assessed qualitatively based on methodological rigor, clarity of reporting, and validation of the outcome tools. Results: All included studies reported improvements in functional status following TKA. Most risk ratios ranged from 0.66 to 0.85, indicating a consistent reduction in the risk of postoperative functional limitation. High-quality studies demonstrated more precise effect estimates and greater internal validity. The SANE scale emerged as a valid and practical tool with high responsiveness, including in its culturally adapted Brazilian version. Despite heterogeneity in study design, the direction of effect remained consistent across all included studies. Conclusion: Validated functional assessment tools are essential for monitoring recovery after total knee arthroplasty. Instruments such as WOMAC and SANE demonstrate strong clinical utility and psychometric validity. Their systematic use enhances outcome comparability, supports individualized rehabilitation planning, and improves decision-making in orthopedic care.

The quality of communication between healthcare providers and pregnant women is a topic of paramount importance within the realm of maternal healthcare. It is not merely an aspect of medical interaction; rather, it is the prerequisite that influences various critical dimensions of maternal care, including maternal satisfaction, health outcomes, and shared decision-making. Effective communication between healthcare providers and pregnant women is essential for optimal maternal care during pregnancy and childbirth. Maternal satisfaction is a fundamental metric of patient-centered care, and improved communication, characterized by empathy, information sharing, and active listening, cultivates trust and enhances women's contentment with their care experiences. Positive provider-patient interactions are associated with improved emotional well-being, reduced stress levels, and increased adherence to prenatal recommendations, contributing to positive health outcomes for both mother and fetus. Shared decision-making is impacted by open and transparent dialogue between healthcare providers and pregnant women. Inclusive discussions about available interventions, risks, and benefits empower women to make informed choices aligned with their preferences and values. This shared decision-making promotes autonomy, self-efficacy, and a collaborative care partnership, potentially influencing the birthing experience and postpartum adaptation. However, challenges persist in communication quality, such as variability in healthcare provider communication styles, cultural considerations, and system-level factors. Addressing these challenges through targeted interventions, training, and policy implementation can further enhance the overall maternal care experience. Further research is needed to explore innovative strategies that optimize communication and promote positive outcomes throughout the continuum of maternal care.

Anaphylaxis can be associated with hemodynamic shock, which requires the early initiation of adrenaline as part of its management. Cardiogenic pulmonary edema is a frequent entity in emergency services with increased mortality in patients with acute coronary syndrome. The case report presents the case of a 55-year-old male patient who entered the emergency department with a non-ST-segment elevation myocardial infarction (NSTEMI) associated to pulmonary edema and anaphylaxis. During his stay in the emergency room, he had an anaphylactic reaction to dipyrone (metamizole) used for pain control. The patient presented signs of acute pulmonary edema, a hypertensive urgency after the use of adrenaline for the management of anaphylaxis.  There was doubt as to whether the dyspnea was of anaphylactic or cardiogenic origin, so an emergency ultrasound was performed, which suggested a bilateral pattern B.  This allowed timely management of ventilatory failure with systemic nitrates, diuretics, and oxygen therapy, which controlled blood pressure and resolved ventilatory failure. Subsequently, he was transferred to an institution with a hemodynamic service for the management of NSTEMI. We highlight the utility of emergency ultrasonography for immediate decision-making and the low prevalence of anaphylactic reaction in a patient with NSTEMI leading to acute pulmonary edema.

Mathematical modeling plays a central role in infectious disease epidemiology, shaping outbreak response strategies and informing public health policy. The COVID-19 pandemic demonstrated the value of these models but also exposed persistent limitations related to data fragility, lack of transparency, limited stakeholder engagement, and insufficient consideration of social and political contexts. Rather than critiquing modeling as a discipline, this perspective argues for a reorientation of infectious disease modeling toward a more responsive, equity-centered, and participatory paradigm. We propose a conceptual framework built on three interrelated principles: adaptability through real-time data integration, transparency via open-source and reproducible practices, and relevance through interdisciplinary and co-produced model design. Drawing on illustrative examples from COVID-19 and dengue control efforts, we highlight how integrating behavioral dynamics, local knowledge, and policy feedback can improve model usefulness and public trust. Reconceptualizing models as dynamic systems of inquiry rather than static forecasting tools can enhance decision-making and promote more equitable and effective responses to future public health emergencies.