Laurence Vaivre-Douret, Clémence Lopez, Audrey Dutruel, Sébastien Vaivre
Phenotyping features in the genesis of pre-scriptural gestures in children to assess handwriting developmental levels
Scientific reports
prognosticating outcomes traumatic brain injury tbi patients challenging due required specialized skills variability among clinicians recent attempts standardize tbi prognosis leveraged machine learning ml methodologies study evaluates necessity influence mlassisted tbi prognostication healthcare professionals perspectives via focus group discussions two virtual focus groups included ten key tbi care stakeholders one neurosurgeon two emergency clinicians one internist two radiologists one registered nurse two researchers ml healthcare one patient representative answered six openended questions perceptions potential ml use tbi prognostication transcribed focus group discussions thematically analyzed using qualitative data analysis software study captured diverse perceptions interests tbi prognostication across clinical specialties notably certain clinicians currently prognosticate expressed interest independently provided access ml support concerns included mls accuracy need proficient ml researchers clinical settings consensus suggested using ml secondary consultation tool promoting collaboration internal external research resources participants believed ml prognostication could enhance disposition planning standardize care regardless clinician expertise injury severity evidence perceived bias interference discussions findings revealed overall positive attitude toward mlbased prognostication despite raising multiple concerns focus group discussions particularly valuable underscoring potential ml democratizing standardizing tbi prognosis practices