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Gianfranco Sanson

Researcher at University of Trieste

Publications -  78
Citations -  1102

Gianfranco Sanson is an academic researcher from University of Trieste. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 16, co-authored 65 publications receiving 788 citations. Previous affiliations of Gianfranco Sanson include University of Rome Tor Vergata & United Hospitals.

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A population-based study on pneumothorax in severely traumatized patients.

TL;DR: PNX can be expected in one in five major trauma victims found alive and is associated with a peculiar on-scene instability, which means early decompression is often required.
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Effects of 2 patterns of prehospital care on the outcome of patients with severe head injury.

TL;DR: This study was conceived to emphasize the supposed advantages of the combined helicopter, physician, and advanced life-support rescue and found no increased benefit compared with the simpler rescue group could be demonstrated.
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Noninvasive techniques for blood pressure measurement are not a reliable alternative to direct measurement: a randomized crossover trial in ICU.

TL;DR: To test the accuracy and reliability of aneroid and oscillometric devices compared to the invasive BP (IBP) monitoring in an ICU population, a randomized crossover trial was conducted.
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Epidemiology of major injury in the population of Friuli Venezia Giulia-Italy

TL;DR: A considerable amount of information on MIJ in FVG has been gathered, of both local and general interest, which can help to assess the local trauma system and also, given the relative scarcity of prospective, population-based information onMIJ, contribute to scientific research.
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Nursing diagnoses, outcomes and interventions as measures of patient complexity and nursing care requirement in Intensive Care Unit.

TL;DR: In critically ill patients, the analysis of nursing diagnoses, outcomes and interventions confirmed an intense activity in response to a broad spectrum of patient needs and allowed to predict patient outcomes.