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Real-world observational pharmacokinetic dataset generated by bedside precision dosing: for every dosed patient episode, the patient characteristics and laboratory values used to individualise therapy, measured drug concentrations, the dose actually administered, the model's predicted exposure and recommended dose, and the resulting target attainment or adverse event. Two layers make this unusually valuable. First, it is field dosing data rather than trial data — dosing decisions and concentrations for real patients in real hospitals, including the special populations (renal impairment, obesity, critical illness, paediatrics) that trials systematically exclude and that drug labels most often lack dosing guidance for. Second, a human-in-the-loop layer: each model dose recommendation carries the clinician's decision to accept or override it, and the platform's continuous-learning process uses accumulated outcomes to retrain the dosing models, so the corpus is already curated at model grade rather than raw.
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