Logo image
Predicting outcomes after sever brain injury: Science, humanity or both?
Journal article   Peer reviewed

Predicting outcomes after sever brain injury: Science, humanity or both?

K.M. Ho
Journal of Neurosurgical Sciences, Vol.62(5), pp.593-598
2018

Abstract

Wounds and injuries Prognosis Trauma, nervous system
Predicting long-term outcome after severe traumatic brain injury (TBI) is difficult, but accurate assessment is paramount for both families of the patients and medical decision-making, as well as quality assurance or research purposes. Many important prognostic factors for patients with severe TBI have been identified, but most — if not all — including the Glasgow Coma Score and magnetic resonance imaging are not accurate enough to be used alone to predict patient outcomes. Clinicians should also be wary about how their predictions and decision-making can be affected by heuristics and cognitive biases. Well-validated prognostic models, including the CRASH and IMPACT models, are easily available and, provided their limitations are appreciated, they offer an enormous potential to assist clinicians to objectively prognosticate the outcomes of patients with severe TBI by reducing the unduly influence of subconscious heuristics and cognitive biases. Finally, we also should not underestimate human being’s adaptability, including their ability to recalibrate what May be acceptable to them when life circumstances have changed. Predicting outcome and decision-making after severe TBI requires a deep understanding of both science and humanity — a task we should all take seriously.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Citation topics
1 Clinical & Life Sciences
1.134 Trauma & Emergency Surgery
1.134.286 Traumatic Brain Injury
Web Of Science research areas
Clinical Neurology
Surgery
ESI research areas
Clinical Medicine
Logo image