Fig. 1

Linear correlation between all pairs of features. To visualize the strength of linear correlation between each pair of features, the value of the Pearson correlation coefficient is represented by the size and colour of the dots in the matrix. Strongly correlated features (pH and base excess, pH and arterial partial pressure of carbon dioxide (PaCO₂), GCS motor and total score, Rotterdam CT score and midline shift, Rotterdam CT score and Fisher classification, GCS motor score and pupil response, age and ASA PS-class (American Society of Anesthesiologists physical status classification), and age and anticoagulants at baseline) were modelled as bivariate joint Gaussian distributions. GCS, Glasgow coma scale; ISS, injury severity score; SpO₂, oxygen saturation; PaO₂, arterial partial pressure of oxygen; PaCO₂, arterial partial pressure of carbon dioxide; BMI, body mass index; TAI, traumatic axonal injury; EDH, epidural hematoma; aSDH, acute subdural hematoma; tSAH, traumatic subarachnoid haemorrhage; MAP, mean arterial pressure; ICP, intracranial pressure; TIL, therapy intensity level