Structured clinical reflection in acute toxicology: bridging guidelines and bedside decision-making

Acute poisoning is a common reason for emergency department presentation and is often associated with diagnostic and therapeutic uncertainty. Although evidence-based guidelines and decision-support tools provide an important framework for management, their application in clinical practice requires contextual interpretation and clinical judgement. This educational reflective analysis was based on observations made during a four-week clinical toxicology observership at the National Poisons Information Service in Edinburgh, United Kingdom. A structured reflective framework derived from Gibbs' reflective cycle was applied to selected clinical encounters. The framework comprised six sequential stages: description, feelings, evaluation, analysis, conclusion, and action planning. Cases were purposively selected to illustrate key domains of acute toxicology decision-making, including ethical complexity and capacity assessment, physiologically guided treatment escalation, and avoidance of unnecessary intervention. Application of the framework facilitated examination of clinical reasoning processes underlying toxicology management. The cases demonstrated how guideline-based knowledge interacts with real-time physiological assessment, ethical considerations, and cognitive factors influencing diagnostic reasoning. Reflection identified potential cognitive biases and highlighted the importance of proportional decision-making in situations of diagnostic uncertainty. Structured clinical reflection provided a practical approach for making implicit clinical reasoning more explicit in acute toxicology practice. By encouraging re-evaluation of assumptions and careful interpretation of clinical findings, the reflective process supported balanced decision-making and appropriate use of guideline-based recommendations. Structured clinical reflection may help clarify clinical reasoning in acute toxicology and support more deliberate and proportionate decision-making under conditions of uncertainty.