Title |
How well do clinical prediction rules perform in identifying serious infections in acutely ill children across an international network of ambulatory care datasets?
|
---|---|
Published in |
BMC Medicine, January 2013
|
DOI | 10.1186/1741-7015-11-10 |
Pubmed ID | |
Authors |
Jan Y Verbakel, Ann Van den Bruel, Matthew Thompson, Richard Stevens, Bert Aertgeerts, Rianne Oostenbrink, Henriette A Moll, Marjolein Y Berger, Monica Lakhanpaul, David Mant, Frank Buntinx, the European Research Network on Recognising Serious Infection (ERNIE) |
Abstract |
Diagnosing serious infections in children is challenging, because of the low incidence of such infections and their non-specific presentation early in the course of illness. Prediction rules are promoted as a means to improve recognition of serious infections. A recent systematic review identified seven clinical prediction rules, of which only one had been prospectively validated, calling into question their appropriateness for clinical practice. We aimed to examine the diagnostic accuracy of these rules in multiple ambulatory care populations in Europe. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 5 | 36% |
Netherlands | 2 | 14% |
Australia | 1 | 7% |
United States | 1 | 7% |
Spain | 1 | 7% |
Canada | 1 | 7% |
Unknown | 3 | 21% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 10 | 71% |
Practitioners (doctors, other healthcare professionals) | 2 | 14% |
Scientists | 2 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
Unknown | 124 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 20 | 16% |
Student > Ph. D. Student | 16 | 13% |
Student > Master | 16 | 13% |
Student > Postgraduate | 14 | 11% |
Student > Bachelor | 11 | 9% |
Other | 29 | 23% |
Unknown | 21 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 76 | 60% |
Nursing and Health Professions | 6 | 5% |
Agricultural and Biological Sciences | 5 | 4% |
Computer Science | 3 | 2% |
Social Sciences | 2 | 2% |
Other | 8 | 6% |
Unknown | 27 | 21% |